fairseq_library_test.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Amplil/nlp-test/blob/master/opmnt_library_transformer_translate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldx06QzFUDmv",
        "outputId": "454f0f73-b29e-4909-ffca-ca63a1ed3f4a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVB_lU8MLAov"
      },
      "source": [
        "# 初期化"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ClLRYsiVAvmO",
        "outputId": "4c2c3823-a5de-484b-cad8-4ce97f5727e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1liJWbv2EhngAj7pixTjzRpv9B-Bz9zCt/Colab Notebooks\n"
          ]
        }
      ],
      "source": [
        "cd \"/content/drive/MyDrive/Colab Notebooks\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "djsObUCTBuU5",
        "outputId": "c7205fa6-528c-471d-cb5a-6826031e62f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CODE_OF_CONDUCT.md  eval_lm.py    \u001b[0m\u001b[01;35mfairseq_logo.png\u001b[0m  score.py  validate.py\n",
            "CONTRIBUTING.md     \u001b[01;34mexamples\u001b[0m/     generate.py       \u001b[01;34mscripts\u001b[0m/\n",
            "LICENSE             \u001b[01;34mfairseq\u001b[0m/      hubconf.py        setup.py\n",
            "README.md           \u001b[01;35mfairseq.gif\u001b[0m   interactive.py    \u001b[01;34mtests\u001b[0m/\n",
            "\u001b[01;34mdocs\u001b[0m/               \u001b[01;34mfairseq_cli\u001b[0m/  preprocess.py     train.py\n"
          ]
        }
      ],
      "source": [
        "ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xxJOKCR4BBvK"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append(\"/content/drive/MyDrive/Colab Notebooks/packages\")\n",
        "import slacknotice # オリジナルモジュール"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'fairseq'...\n",
            "remote: Enumerating objects: 30878, done.\u001b[K\n",
            "remote: Counting objects: 100% (510/510), done.\u001b[K\n",
            "remote: Compressing objects: 100% (313/313), done.\u001b[K\n",
            "remote: Total 30878 (delta 217), reused 393 (delta 190), pack-reused 30368\u001b[K\n",
            "Receiving objects: 100% (30878/30878), 21.21 MiB | 13.58 MiB/s, done.\n",
            "Resolving deltas: 100% (22915/22915), done.\n"
          ]
        }
      ],
      "source": [
        "#!git clone https://github.com/pytorch/fairseq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/home/ubuntu\n"
          ]
        }
      ],
      "source": [
        "cd /home/ubuntu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/home/ubuntu/fairseq\n"
          ]
        }
      ],
      "source": [
        "cd /home/ubuntu/fairseq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/bin/bash: switchml: line 1: syntax error: unexpected end of file\n",
            "/bin/bash: error importing function definition for `switchml'\n",
            "/bin/bash: _moduleraw: line 1: syntax error: unexpected end of file\n",
            "/bin/bash: error importing function definition for `_moduleraw'\n",
            "HEAD is now at c81fed46 Back out \"[fairseq][PR] Fix bug (the returned value has a dimension mismatch) in label-smoothed-cross-entropy for MoE\" (#837)\n"
          ]
        }
      ],
      "source": [
        "#!git checkout c81fed46ac7868c6d80206ff71c6f6cfe93aee22"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/bin/bash: switchml: line 1: syntax error: unexpected end of file\n",
            "/bin/bash: error importing function definition for `switchml'\n",
            "/bin/bash: _moduleraw: line 1: syntax error: unexpected end of file\n",
            "/bin/bash: error importing function definition for `_moduleraw'\n",
            "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n",
            "Collecting torch==1.3.1\n",
            "  Downloading torch-1.3.1-cp36-cp36m-manylinux1_x86_64.whl (734.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 734.6 MB 24 kB/s s eta 0:00:01    |███▍                            | 77.2 MB 72.3 MB/s eta 0:00:10     |███████                         | 159.5 MB 72.9 MB/s eta 0:00:08     |█████████████▋                  | 313.3 MB 67.0 MB/s eta 0:00:07�█▎ | 694.8 MB 81.4 MB/s eta 0:00:01\n",
            "\u001b[?25hRequirement already satisfied: numpy in ./anaconda3/envs/aws_neuron_mxnet_p36/lib/python3.6/site-packages (from torch==1.3.1) (1.16.5)\n",
            "Installing collected packages: torch\n",
            "Successfully installed torch-1.3.1\n"
          ]
        }
      ],
      "source": [
        "!pip install torch==1.3.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/bin/bash: switchml: line 1: syntax error: unexpected end of file\n",
            "/bin/bash: error importing function definition for `switchml'\n",
            "/bin/bash: _moduleraw: line 1: syntax error: unexpected end of file\n",
            "/bin/bash: error importing function definition for `_moduleraw'\n",
            "\u001b[33mWARNING: Skipping fairseq as it is not installed.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!pip uninstall -y fairseq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/home/ubuntu/fairseq\n"
          ]
        }
      ],
      "source": [
        "cd fairseq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/\n"
          ]
        }
      ],
      "source": [
        "cd /"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'/home/ubuntu'"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "running install\n",
            "running bdist_egg\n",
            "running egg_info\n",
            "creating fairseq.egg-info\n",
            "writing fairseq.egg-info/PKG-INFO\n",
            "writing dependency_links to fairseq.egg-info/dependency_links.txt\n",
            "writing entry points to fairseq.egg-info/entry_points.txt\n",
            "writing requirements to fairseq.egg-info/requires.txt\n",
            "writing top-level names to fairseq.egg-info/top_level.txt\n",
            "writing manifest file 'fairseq.egg-info/SOURCES.txt'\n",
            "reading manifest file 'fairseq.egg-info/SOURCES.txt'\n",
            "writing manifest file 'fairseq.egg-info/SOURCES.txt'\n",
            "installing library code to build/bdist.linux-x86_64/egg\n",
            "running install_lib\n",
            "running build_py\n",
            "creating build\n",
            "creating build/lib.linux-x86_64-3.6\n",
            "creating build/lib.linux-x86_64-3.6/fairseq_cli\n",
            "copying fairseq_cli/generate.py -> build/lib.linux-x86_64-3.6/fairseq_cli\n",
            "copying fairseq_cli/setup.py -> build/lib.linux-x86_64-3.6/fairseq_cli\n",
            "copying fairseq_cli/preprocess.py -> build/lib.linux-x86_64-3.6/fairseq_cli\n",
            "copying fairseq_cli/score.py -> build/lib.linux-x86_64-3.6/fairseq_cli\n",
            "copying fairseq_cli/eval_lm.py -> build/lib.linux-x86_64-3.6/fairseq_cli\n",
            "copying fairseq_cli/__init__.py -> build/lib.linux-x86_64-3.6/fairseq_cli\n",
            "copying fairseq_cli/train.py -> build/lib.linux-x86_64-3.6/fairseq_cli\n",
            "copying fairseq_cli/interactive.py -> build/lib.linux-x86_64-3.6/fairseq_cli\n",
            "creating build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/sequence_scorer.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/trainer.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/search.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/checkpoint_utils.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/tokenizer.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/registry.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/utils.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/meters.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/hub_utils.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/pdb.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/sequence_generator.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/options.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/distributed_utils.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/binarizer.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/progress_bar.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/legacy_distributed_data_parallel.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/__init__.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/bleu.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "copying fairseq/file_utils.py -> build/lib.linux-x86_64-3.6/fairseq\n",
            "creating build/lib.linux-x86_64-3.6/examples\n",
            "copying examples/__init__.py -> build/lib.linux-x86_64-3.6/examples\n",
            "creating build/lib.linux-x86_64-3.6/tests\n",
            "creating build/lib.linux-x86_64-3.6/tests/speech_recognition\n",
            "copying tests/speech_recognition/asr_test_base.py -> build/lib.linux-x86_64-3.6/tests/speech_recognition\n",
            "copying tests/speech_recognition/test_vggtransformer.py -> build/lib.linux-x86_64-3.6/tests/speech_recognition\n",
            "copying tests/speech_recognition/__init__.py -> build/lib.linux-x86_64-3.6/tests/speech_recognition\n",
            "copying tests/speech_recognition/test_cross_entropy.py -> build/lib.linux-x86_64-3.6/tests/speech_recognition\n",
            "copying tests/speech_recognition/test_collaters.py -> build/lib.linux-x86_64-3.6/tests/speech_recognition\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/adafactor.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/fairseq_optimizer.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/fp16_optimizer.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/nag.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/adadelta.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/adagrad.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/adamax.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/sgd.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/adam.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "copying fairseq/optim/bmuf.py -> build/lib.linux-x86_64-3.6/fairseq/optim\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/fairseq_encoder.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/transformer.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/lstm.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/multilingual_transformer.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/distributed_fairseq_model.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/fconv_lm.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/transformer_lm.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/fconv.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/wav2vec.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/fairseq_model.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/masked_lm.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/fconv_self_att.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/transformer_from_pretrained_xlm.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/composite_encoder.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/fairseq_decoder.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/lightconv.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/fairseq_incremental_decoder.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "copying fairseq/models/lightconv_lm.py -> build/lib.linux-x86_64-3.6/fairseq/models\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/label_smoothed_cross_entropy.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/sentence_prediction.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/composite_loss.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/legacy_masked_lm.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/fairseq_criterion.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/masked_lm.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/sentence_ranking.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/cross_entropy.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/adaptive_loss.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "copying fairseq/criterions/binary_cross_entropy.py -> build/lib.linux-x86_64-3.6/fairseq/criterions\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/multi_corpus_sampled_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/pad_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/concat_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/plasma_utils.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/strip_token_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/prepend_token_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/indexed_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/id_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/noising.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/dictionary.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/raw_label_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/token_block_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/round_robin_zip_datasets.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/transform_eos_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/base_wrapper_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/numel_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/offset_tokens_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/nested_dictionary_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/iterators.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/data_utils.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/language_pair_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/backtranslation_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/truncate_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/monolingual_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/transform_eos_lang_pair_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/sort_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/fairseq_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/mask_tokens_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/list_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/concat_sentences_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/lru_cache_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/num_samples_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "copying fairseq/data/lm_context_window_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/learned_positional_embedding.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/logsumexp_moe.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/gelu.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/sparse_transformer_sentence_encoder_layer.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/vggblock.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/sparse_multihead_attention.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/unfold.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/conv_tbc.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/beamable_mm.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/sinusoidal_positional_embedding.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/highway.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/positional_embedding.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/character_token_embedder.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/adaptive_input.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/dynamic_convolution.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/transformer_sentence_encoder_layer.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/layer_norm.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/transformer_sentence_encoder.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/linearized_convolution.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/scalar_bias.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/transformer_layer.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/sparse_transformer_sentence_encoder.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/mean_pool_gating_network.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/grad_multiply.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/multihead_attention.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/adaptive_softmax.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/downsampled_multihead_attention.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "copying fairseq/modules/lightweight_convolution.py -> build/lib.linux-x86_64-3.6/fairseq/modules\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/translation_moe.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/fairseq_task.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/translation.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/multilingual_translation.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/sentence_prediction.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/legacy_masked_lm.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/language_modeling.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/masked_lm.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/cross_lingual_lm.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/sentence_ranking.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/translation_from_pretrained_xlm.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/semisupervised_translation.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "copying fairseq/tasks/audio_pretraining.py -> build/lib.linux-x86_64-3.6/fairseq/tasks\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler\n",
            "copying fairseq/optim/lr_scheduler/cosine_lr_scheduler.py -> build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler\n",
            "copying fairseq/optim/lr_scheduler/triangular_lr_scheduler.py -> build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler\n",
            "copying fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py -> build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler\n",
            "copying fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py -> build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler\n",
            "copying fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py -> build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler\n",
            "copying fairseq/optim/lr_scheduler/inverse_square_root_schedule.py -> build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler\n",
            "copying fairseq/optim/lr_scheduler/polynomial_decay_schedule.py -> build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler\n",
            "copying fairseq/optim/lr_scheduler/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler\n",
            "copying fairseq/optim/lr_scheduler/fixed_schedule.py -> build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/models/roberta\n",
            "copying fairseq/models/roberta/model.py -> build/lib.linux-x86_64-3.6/fairseq/models/roberta\n",
            "copying fairseq/models/roberta/hub_interface.py -> build/lib.linux-x86_64-3.6/fairseq/models/roberta\n",
            "copying fairseq/models/roberta/alignment_utils.py -> build/lib.linux-x86_64-3.6/fairseq/models/roberta\n",
            "copying fairseq/models/roberta/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/models/roberta\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/data/audio\n",
            "copying fairseq/data/audio/raw_audio_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data/audio\n",
            "copying fairseq/data/audio/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/data/audio\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "copying fairseq/data/encoders/sentencepiece_bpe.py -> build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "copying fairseq/data/encoders/gpt2_bpe_utils.py -> build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "copying fairseq/data/encoders/fastbpe.py -> build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "copying fairseq/data/encoders/subword_nmt_bpe.py -> build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "copying fairseq/data/encoders/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "copying fairseq/data/encoders/nltk_tokenizer.py -> build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "copying fairseq/data/encoders/space_tokenizer.py -> build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "copying fairseq/data/encoders/moses_tokenizer.py -> build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "copying fairseq/data/encoders/hf_bert_bpe.py -> build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "copying fairseq/data/encoders/gpt2_bpe.py -> build/lib.linux-x86_64-3.6/fairseq/data/encoders\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/data/legacy\n",
            "copying fairseq/data/legacy/masked_lm_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data/legacy\n",
            "copying fairseq/data/legacy/masked_lm_dictionary.py -> build/lib.linux-x86_64-3.6/fairseq/data/legacy\n",
            "copying fairseq/data/legacy/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/data/legacy\n",
            "copying fairseq/data/legacy/block_pair_dataset.py -> build/lib.linux-x86_64-3.6/fairseq/data/legacy\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/modules/lightconv_layer\n",
            "copying fairseq/modules/lightconv_layer/setup.py -> build/lib.linux-x86_64-3.6/fairseq/modules/lightconv_layer\n",
            "copying fairseq/modules/lightconv_layer/lightconv_layer.py -> build/lib.linux-x86_64-3.6/fairseq/modules/lightconv_layer\n",
            "copying fairseq/modules/lightconv_layer/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/modules/lightconv_layer\n",
            "copying fairseq/modules/lightconv_layer/cuda_function_gen.py -> build/lib.linux-x86_64-3.6/fairseq/modules/lightconv_layer\n",
            "creating build/lib.linux-x86_64-3.6/fairseq/modules/dynamicconv_layer\n",
            "copying fairseq/modules/dynamicconv_layer/setup.py -> build/lib.linux-x86_64-3.6/fairseq/modules/dynamicconv_layer\n",
            "copying fairseq/modules/dynamicconv_layer/__init__.py -> build/lib.linux-x86_64-3.6/fairseq/modules/dynamicconv_layer\n",
            "copying fairseq/modules/dynamicconv_layer/cuda_function_gen.py -> build/lib.linux-x86_64-3.6/fairseq/modules/dynamicconv_layer\n",
            "copying fairseq/modules/dynamicconv_layer/dynamicconv_layer.py -> build/lib.linux-x86_64-3.6/fairseq/modules/dynamicconv_layer\n",
            "creating build/lib.linux-x86_64-3.6/examples/speech_recognition\n",
            "copying examples/speech_recognition/infer.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition\n",
            "copying examples/speech_recognition/__init__.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition\n",
            "creating build/lib.linux-x86_64-3.6/examples/noisychannel\n",
            "copying examples/noisychannel/rerank_tune.py -> build/lib.linux-x86_64-3.6/examples/noisychannel\n",
            "copying examples/noisychannel/rerank_options.py -> build/lib.linux-x86_64-3.6/examples/noisychannel\n",
            "copying examples/noisychannel/rerank_utils.py -> build/lib.linux-x86_64-3.6/examples/noisychannel\n",
            "copying examples/noisychannel/rerank.py -> build/lib.linux-x86_64-3.6/examples/noisychannel\n",
            "copying examples/noisychannel/__init__.py -> build/lib.linux-x86_64-3.6/examples/noisychannel\n",
            "copying examples/noisychannel/rerank_generate.py -> build/lib.linux-x86_64-3.6/examples/noisychannel\n",
            "copying examples/noisychannel/rerank_score_lm.py -> build/lib.linux-x86_64-3.6/examples/noisychannel\n",
            "copying examples/noisychannel/rerank_score_bw.py -> build/lib.linux-x86_64-3.6/examples/noisychannel\n",
            "creating build/lib.linux-x86_64-3.6/examples/speech_recognition/models\n",
            "copying examples/speech_recognition/models/__init__.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition/models\n",
            "copying examples/speech_recognition/models/vggtransformer.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition/models\n",
            "creating build/lib.linux-x86_64-3.6/examples/speech_recognition/criterions\n",
            "copying examples/speech_recognition/criterions/__init__.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition/criterions\n",
            "copying examples/speech_recognition/criterions/cross_entropy_acc.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition/criterions\n",
            "creating build/lib.linux-x86_64-3.6/examples/speech_recognition/data\n",
            "copying examples/speech_recognition/data/collaters.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition/data\n",
            "copying examples/speech_recognition/data/asr_dataset.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition/data\n",
            "copying examples/speech_recognition/data/data_utils.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition/data\n",
            "copying examples/speech_recognition/data/__init__.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition/data\n",
            "creating build/lib.linux-x86_64-3.6/examples/speech_recognition/tasks\n",
            "copying examples/speech_recognition/tasks/__init__.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition/tasks\n",
            "copying examples/speech_recognition/tasks/speech_recognition.py -> build/lib.linux-x86_64-3.6/examples/speech_recognition/tasks\n",
            "running build_ext\n",
            "building 'fairseq.libbleu' extension\n",
            "creating build/temp.linux-x86_64-3.6\n",
            "creating build/temp.linux-x86_64-3.6/fairseq\n",
            "creating build/temp.linux-x86_64-3.6/fairseq/clib\n",
            "creating build/temp.linux-x86_64-3.6/fairseq/clib/libbleu\n",
            "gcc -pthread -B /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/include/python3.6m -c fairseq/clib/libbleu/libbleu.cpp -o build/temp.linux-x86_64-3.6/fairseq/clib/libbleu/libbleu.o -std=c++11\n",
            "\u001b[01m\u001b[Kcc1plus:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcommand line option ‘\u001b[01m\u001b[K-Wstrict-prototypes\u001b[m\u001b[K’ is valid for C/ObjC but not for C++\n",
            "gcc -pthread -B /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/include/python3.6m -c fairseq/clib/libbleu/module.cpp -o build/temp.linux-x86_64-3.6/fairseq/clib/libbleu/module.o -std=c++11\n",
            "\u001b[01m\u001b[Kcc1plus:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcommand line option ‘\u001b[01m\u001b[K-Wstrict-prototypes\u001b[m\u001b[K’ is valid for C/ObjC but not for C++\n",
            "g++ -pthread -shared -B /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/compiler_compat -L/home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib -Wl,-rpath=/home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.6/fairseq/clib/libbleu/libbleu.o build/temp.linux-x86_64-3.6/fairseq/clib/libbleu/module.o -o build/lib.linux-x86_64-3.6/fairseq/libbleu.cpython-36m-x86_64-linux-gnu.so\n",
            "creating build/bdist.linux-x86_64\n",
            "creating build/bdist.linux-x86_64/egg\n",
            "creating build/bdist.linux-x86_64/egg/tests\n",
            "creating build/bdist.linux-x86_64/egg/tests/speech_recognition\n",
            "copying build/lib.linux-x86_64-3.6/tests/speech_recognition/asr_test_base.py -> build/bdist.linux-x86_64/egg/tests/speech_recognition\n",
            "copying build/lib.linux-x86_64-3.6/tests/speech_recognition/test_vggtransformer.py -> build/bdist.linux-x86_64/egg/tests/speech_recognition\n",
            "copying build/lib.linux-x86_64-3.6/tests/speech_recognition/__init__.py -> build/bdist.linux-x86_64/egg/tests/speech_recognition\n",
            "copying build/lib.linux-x86_64-3.6/tests/speech_recognition/test_cross_entropy.py -> build/bdist.linux-x86_64/egg/tests/speech_recognition\n",
            "copying build/lib.linux-x86_64-3.6/tests/speech_recognition/test_collaters.py -> build/bdist.linux-x86_64/egg/tests/speech_recognition\n",
            "creating build/bdist.linux-x86_64/egg/fairseq_cli\n",
            "copying build/lib.linux-x86_64-3.6/fairseq_cli/generate.py -> build/bdist.linux-x86_64/egg/fairseq_cli\n",
            "copying build/lib.linux-x86_64-3.6/fairseq_cli/setup.py -> build/bdist.linux-x86_64/egg/fairseq_cli\n",
            "copying build/lib.linux-x86_64-3.6/fairseq_cli/preprocess.py -> build/bdist.linux-x86_64/egg/fairseq_cli\n",
            "copying build/lib.linux-x86_64-3.6/fairseq_cli/score.py -> build/bdist.linux-x86_64/egg/fairseq_cli\n",
            "copying build/lib.linux-x86_64-3.6/fairseq_cli/eval_lm.py -> build/bdist.linux-x86_64/egg/fairseq_cli\n",
            "copying build/lib.linux-x86_64-3.6/fairseq_cli/__init__.py -> build/bdist.linux-x86_64/egg/fairseq_cli\n",
            "copying build/lib.linux-x86_64-3.6/fairseq_cli/train.py -> build/bdist.linux-x86_64/egg/fairseq_cli\n",
            "copying build/lib.linux-x86_64-3.6/fairseq_cli/interactive.py -> build/bdist.linux-x86_64/egg/fairseq_cli\n",
            "creating build/bdist.linux-x86_64/egg/fairseq\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/sequence_scorer.py -> build/bdist.linux-x86_64/egg/fairseq\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/trainer.py -> build/bdist.linux-x86_64/egg/fairseq\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/search.py -> build/bdist.linux-x86_64/egg/fairseq\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/checkpoint_utils.py -> build/bdist.linux-x86_64/egg/fairseq\n",
            "creating build/bdist.linux-x86_64/egg/fairseq/optim\n",
            "creating build/bdist.linux-x86_64/egg/fairseq/optim/lr_scheduler\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py -> build/bdist.linux-x86_64/egg/fairseq/optim/lr_scheduler\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py -> build/bdist.linux-x86_64/egg/fairseq/optim/lr_scheduler\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py -> build/bdist.linux-x86_64/egg/fairseq/optim/lr_scheduler\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py -> build/bdist.linux-x86_64/egg/fairseq/optim/lr_scheduler\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py -> build/bdist.linux-x86_64/egg/fairseq/optim/lr_scheduler\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py -> build/bdist.linux-x86_64/egg/fairseq/optim/lr_scheduler\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py -> build/bdist.linux-x86_64/egg/fairseq/optim/lr_scheduler\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler/__init__.py -> build/bdist.linux-x86_64/egg/fairseq/optim/lr_scheduler\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/lr_scheduler/fixed_schedule.py -> build/bdist.linux-x86_64/egg/fairseq/optim/lr_scheduler\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/adafactor.py -> build/bdist.linux-x86_64/egg/fairseq/optim\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/fairseq_optimizer.py -> build/bdist.linux-x86_64/egg/fairseq/optim\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/fp16_optimizer.py -> build/bdist.linux-x86_64/egg/fairseq/optim\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/nag.py -> build/bdist.linux-x86_64/egg/fairseq/optim\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/adadelta.py -> build/bdist.linux-x86_64/egg/fairseq/optim\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/adagrad.py -> build/bdist.linux-x86_64/egg/fairseq/optim\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/adamax.py -> build/bdist.linux-x86_64/egg/fairseq/optim\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/sgd.py -> build/bdist.linux-x86_64/egg/fairseq/optim\n",
            "copying build/lib.linux-x86_64-3.6/fairseq/optim/__init__.py -> build/bdist.linux-x86_64/egg/fairseq/optim\n",
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            "creating 'dist/fairseq-0.8.0-py3.6-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
            "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
            "Processing fairseq-0.8.0-py3.6-linux-x86_64.egg\n",
            "creating /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/fairseq-0.8.0-py3.6-linux-x86_64.egg\n",
            "Extracting fairseq-0.8.0-py3.6-linux-x86_64.egg to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Adding fairseq 0.8.0 to easy-install.pth file\n",
            "Installing fairseq-eval-lm script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "Installing fairseq-generate script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "Installing fairseq-interactive script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "Installing fairseq-preprocess script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "Installing fairseq-score script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "Installing fairseq-train script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "Installing fairseq-validate script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "\n",
            "Installed /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/fairseq-0.8.0-py3.6-linux-x86_64.egg\n",
            "Processing dependencies for fairseq==0.8.0\n",
            "Searching for sacrebleu\n",
            "Reading https://pypi.org/simple/sacrebleu/\n",
            "Downloading https://files.pythonhosted.org/packages/fa/63/b3c11f951eafa2dc296862431f29fb12dbe191cb72217cf88ed04c32086b/sacrebleu-2.0.0-py3-none-any.whl#sha256=1acae0221e27c23c4987834fd17b284b4addc6556941c2097c4d618baa2d67af\n",
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            "Installing sacrebleu-2.0.0-py3-none-any.whl to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Adding sacrebleu 2.0.0 to easy-install.pth file\n",
            "Installing sacrebleu script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "\n",
            "Installed /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/sacrebleu-2.0.0-py3.6.egg\n",
            "Searching for fastBPE\n",
            "Reading https://pypi.org/simple/fastBPE/\n",
            "Downloading https://files.pythonhosted.org/packages/e1/37/f97181428a5d151501b90b2cebedf97c81b034ace753606a3cda5ad4e6e2/fastBPE-0.1.0.tar.gz#sha256=95eef4be2689e822a918ac4eae3349cd78ca3f28af591afa421f8fac6d4cd889\n",
            "Best match: fastBPE 0.1.0\n",
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            "Writing /tmp/easy_install-388xvcj0/fastBPE-0.1.0/setup.cfg\n",
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            "Compiling fastBPE/fastBPE.pyx because it depends on /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/Cython/Includes/libcpp/vector.pxd.\n",
            "[1/1] Cythonizing fastBPE/fastBPE.pyx\n",
            "/home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /tmp/easy_install-388xvcj0/fastBPE-0.1.0/fastBPE/fastBPE.pyx\n",
            "  tree = Parsing.p_module(s, pxd, full_module_name)\n",
            "package init file 'fastBPE/__init__.py' not found (or not a regular file)\n",
            "\u001b[01m\u001b[Kcc1plus:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcommand line option ‘\u001b[01m\u001b[K-Wstrict-prototypes\u001b[m\u001b[K’ is valid for C/ObjC but not for C++\n",
            "zip_safe flag not set; analyzing archive contents...\n",
            "__pycache__.fastBPE.cpython-36: module references __file__\n",
            "creating /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/fastBPE-0.1.0-py3.6-linux-x86_64.egg\n",
            "Extracting fastBPE-0.1.0-py3.6-linux-x86_64.egg to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Adding fastBPE 0.1.0 to easy-install.pth file\n",
            "\n",
            "Installed /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/fastBPE-0.1.0-py3.6-linux-x86_64.egg\n",
            "Searching for portalocker\n",
            "Reading https://pypi.org/simple/portalocker/\n",
            "Downloading https://files.pythonhosted.org/packages/63/eb/f84872af6e9312ea2f345b218015a41191cfd37eeba4a4fd228f241c2a75/portalocker-2.3.2-py2.py3-none-any.whl#sha256=d8c9f7c542e768dbef006a3e49875046ca170d2d41ca712080719110bd066cc4\n",
            "Best match: portalocker 2.3.2\n",
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            "Installing portalocker-2.3.2-py2.py3-none-any.whl to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Adding portalocker 2.3.2 to easy-install.pth file\n",
            "\n",
            "Installed /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/portalocker-2.3.2-py3.6.egg\n",
            "Searching for tqdm==4.62.3\n",
            "Best match: tqdm 4.62.3\n",
            "Adding tqdm 4.62.3 to easy-install.pth file\n",
            "Installing tqdm script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "\n",
            "Using /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Searching for torch==1.10.2\n",
            "Best match: torch 1.10.2\n",
            "Adding torch 1.10.2 to easy-install.pth file\n",
            "Installing convert-caffe2-to-onnx script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "Installing convert-onnx-to-caffe2 script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "Installing torchrun script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "\n",
            "Using /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Searching for regex==2020.11.13\n",
            "Best match: regex 2020.11.13\n",
            "Adding regex 2020.11.13 to easy-install.pth file\n",
            "\n",
            "Using /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Searching for numpy==1.19.5\n",
            "Best match: numpy 1.19.5\n",
            "Adding numpy 1.19.5 to easy-install.pth file\n",
            "Installing f2py script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "Installing f2py3 script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "Installing f2py3.6 script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "\n",
            "Using /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Searching for cffi==1.14.5\n",
            "Best match: cffi 1.14.5\n",
            "Adding cffi 1.14.5 to easy-install.pth file\n",
            "\n",
            "Using /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Searching for dataclasses==0.8\n",
            "Best match: dataclasses 0.8\n",
            "Adding dataclasses 0.8 to easy-install.pth file\n",
            "\n",
            "Using /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Searching for typing-extensions==3.10.0.2\n",
            "Best match: typing-extensions 3.10.0.2\n",
            "Adding typing-extensions 3.10.0.2 to easy-install.pth file\n",
            "\n",
            "Using /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Searching for tabulate==0.8.9\n",
            "Best match: tabulate 0.8.9\n",
            "Adding tabulate 0.8.9 to easy-install.pth file\n",
            "Installing tabulate script to /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/bin\n",
            "\n",
            "Using /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Searching for colorama==0.4.4\n",
            "Best match: colorama 0.4.4\n",
            "Adding colorama 0.4.4 to easy-install.pth file\n",
            "\n",
            "Using /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Searching for pycparser==2.20\n",
            "Best match: pycparser 2.20\n",
            "Adding pycparser 2.20 to easy-install.pth file\n",
            "\n",
            "Using /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages\n",
            "Finished processing dependencies for fairseq==0.8.0\n"
          ]
        }
      ],
      "source": [
        "!python3 setup.py install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "ename": "ImportError",
          "evalue": "cannot import name 'utils'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-49cb8a7dd707>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfairseq\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m: cannot import name 'utils'"
          ]
        }
      ],
      "source": [
        "from fairseq import utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {},
      "outputs": [],
      "source": [
        "import fairseq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [
        {
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'fairseq.models'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-f29cacacdadb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mfairseq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fairseq.models'"
          ]
        }
      ],
      "source": [
        "import models.transformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'fairseq.models'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-cfcceab89b7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfairseq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransformer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTransformerModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fairseq.models'"
          ]
        }
      ],
      "source": [
        "from fairseq.models.transformer import TransformerModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'fairseq.models'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-c73ee139a516>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfairseq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransformer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTransformerModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#from transformer import TransformerModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fairseq.models'"
          ]
        }
      ],
      "source": [
        "from fairseq.models.transformer import TransformerModel\n",
        "#from transformer import TransformerModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'/home/ubuntu/fairseq'"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/home/ubuntu/jparacrawl-experiments/jparacrawl-finetune\n"
          ]
        }
      ],
      "source": [
        "cd /home/ubuntu/jparacrawl-experiments/jparacrawl-finetune"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loading archive file /home/ubuntu/jparacrawl-experiments/jparacrawl-finetune/pretrained_model_jaen\n",
            "loading archive file /home/ubuntu/jparacrawl-experiments/jparacrawl-finetune/data-sample-bin/decode\n",
            "| [ja] dictionary: 31960 types\n",
            "| [en] dictionary: 31968 types\n",
            "Namespace(activation_dropout=0.0, activation_fn='relu', adam_betas='(0.9, 0.98)', adam_eps=1e-08, adaptive_input=False, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, arch='transformer', attention_dropout=0.0, best_checkpoint_metric='loss', bpe=None, bucket_cap_mb=25, clip_norm=1.0, cpu=False, criterion='label_smoothed_cross_entropy', curriculum=0, data='/home/ubuntu/jparacrawl-experiments/jparacrawl-finetune/data-sample-bin/decode', dataset_impl=None, ddp_backend='no_c10d', decoder_attention_heads=8, decoder_embed_dim=512, decoder_embed_path=None, decoder_ffn_embed_dim=2048, decoder_input_dim=512, decoder_layers=6, decoder_learned_pos=False, decoder_normalize_before=False, decoder_output_dim=512, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method='tcp://localhost:13137', distributed_no_spawn=False, distributed_port=-1, distributed_rank=0, distributed_world_size=8, dropout=0.3, encoder_attention_heads=8, encoder_embed_dim=512, encoder_embed_path=None, encoder_ffn_embed_dim=2048, encoder_layers=6, encoder_learned_pos=False, encoder_normalize_before=False, find_unused_parameters=False, fix_batches_to_gpus=False, fp16=True, fp16_init_scale=128, fp16_scale_tolerance=0.0, fp16_scale_window=None, keep_interval_updates=8, keep_last_epochs=-1, label_smoothing=0.1, lazy_load=False, left_pad_source=False, left_pad_target=False, log_format='simple', log_interval=5, lr=[0.001], lr_scheduler='inverse_sqrt', max_epoch=0, max_sentences=None, max_sentences_valid=None, max_source_positions=1024, max_target_positions=1024, max_tokens=5000, max_tokens_valid=5000, max_update=24000, maximize_best_checkpoint_metric=False, memory_efficient_fp16=False, min_loss_scale=0.0001, min_lr=1e-09, no_epoch_checkpoints=True, no_last_checkpoints=False, no_progress_bar=False, no_save=False, no_save_optimizer_state=False, no_token_positional_embeddings=False, num_workers=1, optimizer='adam', optimizer_overrides='{}', raw_text=False, required_batch_size_multiple=8, reset_dataloader=False, reset_lr_scheduler=False, reset_meters=False, reset_optimizer=False, restore_file='checkpoint_last.pt', save_dir='/sougen/morishita/work19/research/project/2019/paracrawl/190903_crawl_model_jaen/train/models/base_baseline_seed1', save_interval=10000000000, save_interval_updates=200, seed=1, sentence_avg=False, share_all_embeddings=False, share_decoder_input_output_embed=False, skip_invalid_size_inputs_valid_test=False, source_lang='ja', target_lang='en', task='translation', tbmf_wrapper=False, tensorboard_logdir='', threshold_loss_scale=None, tokenizer=None, train_subset='train', update_freq=[8], upsample_primary=1, use_bmuf=False, user_dir=None, valid_subset='valid', validate_interval=1000000000, warmup_init_lr=1e-07, warmup_updates=4000, weight_decay=0.0)\n"
          ]
        }
      ],
      "source": [
        "model = TransformerModel.from_pretrained('.','./pretrained_model_jaen/base.pretrain.pt','./data-sample-bin/decode')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "ename": "RuntimeError",
          "evalue": "result type Float can't be cast to the desired output type Long",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-8d05cf73a342>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'▁ トム は 猫 が ほしい の よ 。'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbeam\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/home/ubuntu/fairseq/fairseq/hub_utils.py\u001b[0m in \u001b[0;36mtranslate\u001b[0;34m(self, sentence, beam, verbose, **kwargs)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtranslate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msentence\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeam\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msentence\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeam\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/home/ubuntu/fairseq/fairseq/hub_utils.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, sentence, beam, verbose, **kwargs)\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msentence\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeam\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m         \u001b[0mhypo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tokens'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhypo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/home/ubuntu/fairseq/fairseq/hub_utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, tokens, beam, verbose, **kwargs)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0mgenerator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mtranslations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/home/ubuntu/fairseq/fairseq/tasks/fairseq_task.py\u001b[0m in \u001b[0;36minference_step\u001b[0;34m(self, generator, models, sample, prefix_tokens)\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minference_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprefix_tokens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mupdate_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_updates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/home/ubuntu/fairseq/fairseq/sequence_generator.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, models, sample, prefix_tokens, bos_token, **kwargs)\u001b[0m\n\u001b[1;32m    374\u001b[0m                 \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m                 \u001b[0mlprobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbsz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m                 \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbsz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeam_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    377\u001b[0m             )\n\u001b[1;32m    378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/home/ubuntu/fairseq/fairseq/search.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, step, lprobs, scores)\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices_buf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         )\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocab_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbeams_buf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices_buf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmod_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvocab_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbeams_buf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: result type Float can't be cast to the desired output type Long"
          ]
        }
      ],
      "source": [
        "model.translate('▁ トム は 猫 が ほしい の よ 。',beam=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model.translate('▁ トム は 猫 が ほしい の よ 。',beam=5,attention=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "tokens =model.encode('▁ トム は 猫 が ほしい の よ 。')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "tokens =model.encode('▁Tom ▁wants ▁a ▁cat .')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model.extract_features('▁ トム は 猫 が ほしい の よ 。')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtInB14qBNkU"
      },
      "source": [
        "# モデルの読み込み"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umQJYyFWsvAy",
        "outputId": "ad3095d4-b718-4242-9e79-c317297e992e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Namespace(align_debug=False, alpha=0.0, attn_debug=True, avg_raw_probs=False, ban_unk_token=False, batch_size=30, batch_type='sents', beam_size=5, beta=-0.0, block_ngram_repeat=0, coverage_penalty='none', data_type='text', dump_beam='', fp32=False, gpu=0, ignore_when_blocking=[], int8=False, length_penalty='none', log_file='', log_file_level='0', max_length=100, max_sent_length=None, min_length=0, models=['data/kftt-data-1.0/run/model_step_15000.pt'], n_best=1, output='data/kftt-data-1.0/out.txt', phrase_table='', random_sampling_temp=1.0, random_sampling_topk=0, random_sampling_topp=0.0, ratio=-0.0, replace_unk=False, report_align=False, report_time=False, seed=-1, shard_size=10000, src='data/en_sample.txt', src_feats=None, stepwise_penalty=False, tgt=None, tgt_prefix=False, verbose=True)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import onmt.opts as opts\n",
        "from onmt.utils.parse import ArgumentParser\n",
        "#from onmt.opts import dynamic_prepare_opts\n",
        "\n",
        "parser = ArgumentParser(description='translate.py')\n",
        "#dynamic_prepare_opts(parser, build_vocab_only=True)\n",
        "opts.translate_opts(parser)\n",
        "\n",
        "base_args = ([\"-model\", \"data/kftt-data-1.0/run/model_step_15000.pt\", \"-src\", \"data/en_sample.txt\", \"-output\", \"data/kftt-data-1.0/out.txt\", \"-gpu\", \"0\", \"-verbose\",\"-attn_debug\"])\n",
        "opt, unknown = parser.parse_known_args(base_args)\n",
        "opt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cHcg9gkUNs7V"
      },
      "outputs": [],
      "source": [
        "from onmt.translate.translator import build_translator\n",
        "\n",
        "ArgumentParser.validate_translate_opts(opt)\n",
        "logger = init_logger(opt.log_file)\n",
        "\n",
        "translator = build_translator(opt, logger=logger, report_score=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y1njxC7MJ8u_"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "# out_fileの作成\n",
        "if out_file is None:\n",
        "    out_file = codecs.open(opt.output, \"w+\", \"utf-8\")\n",
        "\"\"\"\n",
        "load_test_model = (\n",
        "    onmt.decoders.ensemble.load_test_model\n",
        "    if len(opt.models) > 1\n",
        "    else onmt.model_builder.load_test_model\n",
        ")\n",
        "vocab_fields, model, model_opt = load_test_model(opt) # fields->vocab_fields\n",
        "\n",
        "scorer = onmt.translate.GNMTGlobalScorer.from_opt(opt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "i9WIABBdhY0K",
        "outputId": "f5f71e87-3016-4a31-94c6-9c5b23aba3b4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nfrom onmt.utils.parse import ArgumentParser\\nimport onmt.inputters as inputters\\nfrom onmt.utils.misc import use_gpu\\n\\nmodel_path=\"data/kftt-data-1.0/run/model_step_15000.pt\"\\n\\ncheckpoint = torch.load(model_path,map_location=lambda storage, loc: storage)\\n\\nmodel_opt = ArgumentParser.ckpt_model_opts(checkpoint[\\'opt\\'])\\nArgumentParser.update_model_opts(model_opt)\\nArgumentParser.validate_model_opts(model_opt)\\nfields = checkpoint[\\'vocab\\']\\n\\n# Avoid functionality on inference\\nmodel_opt.update_vocab = False\\n\\nmodel = onmt.model_builder.build_base_model(model_opt, fields, use_gpu(opt), checkpoint,\\n                            opt.gpu)\\nif opt.fp32:\\n    model.float()\\nelif opt.int8:\\n    if opt.gpu >= 0:\\n        raise ValueError(\\n            \"Dynamic 8-bit quantization is not supported on GPU\")\\n    torch.quantization.quantize_dynamic(model, inplace=True)\\nmodel.eval()\\nmodel.generator.eval()\\n'"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#def load_test_model(opt, model_path=None)\n",
        "\"\"\"\n",
        "from onmt.utils.parse import ArgumentParser\n",
        "import onmt.inputters as inputters\n",
        "from onmt.utils.misc import use_gpu\n",
        "\n",
        "model_path=\"data/kftt-data-1.0/run/model_step_15000.pt\"\n",
        "\n",
        "checkpoint = torch.load(model_path,map_location=lambda storage, loc: storage)\n",
        "\n",
        "model_opt = ArgumentParser.ckpt_model_opts(checkpoint['opt'])\n",
        "ArgumentParser.update_model_opts(model_opt)\n",
        "ArgumentParser.validate_model_opts(model_opt)\n",
        "fields = checkpoint['vocab']\n",
        "\n",
        "# Avoid functionality on inference\n",
        "model_opt.update_vocab = False\n",
        "\n",
        "model = onmt.model_builder.build_base_model(model_opt, fields, use_gpu(opt), checkpoint,\n",
        "                            opt.gpu)\n",
        "if opt.fp32:\n",
        "    model.float()\n",
        "elif opt.int8:\n",
        "    if opt.gpu >= 0:\n",
        "        raise ValueError(\n",
        "            \"Dynamic 8-bit quantization is not supported on GPU\")\n",
        "    torch.quantization.quantize_dynamic(model, inplace=True)\n",
        "model.eval()\n",
        "model.generator.eval()\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IGDQWf9HgzVn",
        "outputId": "7bc47a92-9c30-40e1-eb08-91656ecd9d63"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "NMTModel(\n",
              "  (encoder): TransformerEncoder(\n",
              "    (embeddings): Embeddings(\n",
              "      (make_embedding): Sequential(\n",
              "        (emb_luts): Elementwise(\n",
              "          (0): Embedding(13639, 512, padding_idx=1)\n",
              "        )\n",
              "        (pe): PositionalEncoding(\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (transformer): ModuleList(\n",
              "      (0): TransformerEncoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (dropout): Dropout(p=0.1, inplace=False)\n",
              "      )\n",
              "      (1): TransformerEncoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (dropout): Dropout(p=0.1, inplace=False)\n",
              "      )\n",
              "      (2): TransformerEncoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (dropout): Dropout(p=0.1, inplace=False)\n",
              "      )\n",
              "      (3): TransformerEncoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (dropout): Dropout(p=0.1, inplace=False)\n",
              "      )\n",
              "      (4): TransformerEncoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (dropout): Dropout(p=0.1, inplace=False)\n",
              "      )\n",
              "      (5): TransformerEncoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (dropout): Dropout(p=0.1, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "  )\n",
              "  (decoder): TransformerDecoder(\n",
              "    (embeddings): Embeddings(\n",
              "      (make_embedding): Sequential(\n",
              "        (emb_luts): Elementwise(\n",
              "          (0): Embedding(12203, 512, padding_idx=1)\n",
              "        )\n",
              "        (pe): PositionalEncoding(\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "    (transformer_layers): ModuleList(\n",
              "      (0): TransformerDecoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm_1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (drop): Dropout(p=0.1, inplace=False)\n",
              "        (context_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (layer_norm_2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "      )\n",
              "      (1): TransformerDecoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm_1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (drop): Dropout(p=0.1, inplace=False)\n",
              "        (context_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (layer_norm_2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "      )\n",
              "      (2): TransformerDecoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm_1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (drop): Dropout(p=0.1, inplace=False)\n",
              "        (context_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (layer_norm_2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "      )\n",
              "      (3): TransformerDecoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm_1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (drop): Dropout(p=0.1, inplace=False)\n",
              "        (context_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (layer_norm_2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "      )\n",
              "      (4): TransformerDecoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm_1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (drop): Dropout(p=0.1, inplace=False)\n",
              "        (context_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (layer_norm_2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "      )\n",
              "      (5): TransformerDecoderLayer(\n",
              "        (self_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (feed_forward): PositionwiseFeedForward(\n",
              "          (w_1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (w_2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "          (dropout_1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout_2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (layer_norm_1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "        (drop): Dropout(p=0.1, inplace=False)\n",
              "        (context_attn): MultiHeadedAttention(\n",
              "          (linear_keys): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_values): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (linear_query): Linear(in_features=512, out_features=512, bias=True)\n",
              "          (softmax): Softmax(dim=-1)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (final_linear): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (layer_norm_2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (generator): Sequential(\n",
              "    (0): Linear(in_features=512, out_features=12203, bias=True)\n",
              "    (1): Cast()\n",
              "    (2): LogSoftmax(dim=-1)\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VAzsuUhFikbY"
      },
      "outputs": [],
      "source": [
        "import onmt.inputters as inputters\n",
        "src_reader = inputters.str2reader[opt.data_type].from_opt(opt)\n",
        "tgt_reader = inputters.str2reader[\"text\"].from_opt(opt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TaD5jFBZaa8k"
      },
      "outputs": [],
      "source": [
        "from onmt.translate.translator import max_tok_len\n",
        "\n",
        "src_data = {\n",
        "    \"reader\": src_reader, # self.src_reader->src_reader\n",
        "    \"data\": opt.src, # src->opt.src\n",
        "    \"features\": {} # src_feats->{}\n",
        "}\n",
        "tgt_data = {\n",
        "    \"reader\": tgt_reader, # self.tgt_reader->tgt_reader\n",
        "    \"data\": opt.tgt, # tgt->opt.tgt\n",
        "    \"features\": {}\n",
        "}\n",
        "_readers, _data = inputters.Dataset.config(\n",
        "    [(\"src\", src_data), (\"tgt\", tgt_data)]\n",
        ")\n",
        "\n",
        "dataset = inputters.Dataset( # data->dataset\n",
        "    vocab_fields, # self.fields->vocab_fields\n",
        "    readers=_readers,\n",
        "    data=_data,\n",
        "    sort_key=inputters.str2sortkey[opt.data_type], # self->opt\n",
        "    filter_pred=None, # self._filter_pred -> None\n",
        ")\n",
        "\n",
        "data_iter = inputters.OrderedIterator(\n",
        "    dataset=dataset, # data->dataset\n",
        "    device=\"cuda\", # self._dev->\"cuda\"\n",
        "    batch_size=opt.batch_size, # batch_size->opt.batch_size\n",
        "    batch_size_fn=max_tok_len if opt.batch_type == \"tokens\" else None, # batch_type->opt.batch_type\n",
        "    train=False,\n",
        "    sort=False,\n",
        "    sort_within_batch=True,\n",
        "    shuffle=False,\n",
        ")\n",
        "\n",
        "builder = onmt.translate.TranslationBuilder(data=dataset, fields=vocab_fields)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZ6AiX9W8_OE"
      },
      "source": [
        "# アテンションの利用"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lm-GL0PZO5Us",
        "outputId": "79072951-e416-41cb-e57a-0314582ef388"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/onmt/translate/beam_search.py:282: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  self._batch_index = self.topk_ids // vocab_size\n",
            "[2021-11-12 03:37:47,165 INFO]                   Tom      wants          a        cat          . \n",
            "     <unk>  0.0810000  0.1024836  0.2358941 *0.3071590  0.2734634 \n",
            "         は  0.0732654  0.2247687  0.1639529  0.1544976 *0.3835154 \n",
            "     <unk>  0.0123236  0.0434103  0.2558493 *0.5182254  0.1701914 \n",
            "         を  0.0581806  0.1314417  0.1406690  0.1174424 *0.5522661 \n",
            "        求め  0.0094350  0.0870038  0.1193642  0.1103888 *0.6738082 \n",
            "         た  0.0225705  0.0063565  0.0122755  0.0420761 *0.9167213 \n",
            "         。  0.0304229  0.0092131  0.0123016  0.0236017 *0.9244607 \n",
            "      </s>  0.0484912  0.0379364  0.0628432  0.0896175 *0.7611117 \n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "SENT 0: ['Tom', 'wants', 'a', 'cat', '.']\n",
            "PRED 0: <unk> は <unk> を 求め た 。\n",
            "PRED SCORE: -5.6415\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from onmt.constants import DefaultTokens\n",
        "from onmt.utils.misc import tile, set_random_seed, report_matrix\n",
        "\n",
        "# アテンションデバッグをON->今後はopt.attn_debugを使う\n",
        "# attn_debug=True\n",
        "for batch in data_iter:\n",
        "    batch_data = translator.translate_batch(\n",
        "        batch, dataset.src_vocabs, opt.attn_debug\n",
        "    )\n",
        "    translations = builder.from_batch(batch_data)\n",
        "\n",
        "    for trans in translations:\n",
        "        print(trans.log(0))\n",
        "        \n",
        "        if opt.attn_debug:\n",
        "            preds = trans.pred_sents[0]\n",
        "            preds.append(DefaultTokens.EOS)\n",
        "            attns = trans.attns[0].tolist()\n",
        "            if translator.data_type == \"text\":\n",
        "                srcs = trans.src_raw\n",
        "            else:\n",
        "                srcs = [str(item) for item in range(len(attns[0]))]\n",
        "            output = report_matrix(srcs, preds, attns)\n",
        "            if translator.logger:\n",
        "                translator.logger.info(output)\n",
        "            else:\n",
        "                #os.write(1, output.encode(\"utf-8\"))\n",
        "                print(output)\n",
        "    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rm63hRsZn4So",
        "outputId": "40d3cba9-cd0d-4146-885a-4bd1deffcbba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "opt.attn_debug"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "include_colab_link": true,
      "name": "opmnt_library_transformer_translate.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Environment (conda_amazonei_mxnet_p36)",
      "language": "python",
      "name": "conda_amazonei_mxnet_p36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}