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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JonathanSum/Hugging-Face-Course/blob/main/Ch3_A_full_training_TPU.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UsRs-JJL0B46"
      },
      "source": [
        "# A full training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Chv-c9Fc0B48"
      },
      "source": [
        "Install the Transformers and Datasets libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3moON5EP0B48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94c1e12c-2250-4737-d146-edd83f9924bc"
      },
      "source": [
        "! pip install datasets transformers[sentencepiece]\n",
        "! pip install accelerate cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-1.11.0-py3-none-any.whl (264 kB)\n",
            "\u001b[K     |████████████████████████████████| 264 kB 5.4 MB/s \n",
            "\u001b[?25hCollecting transformers[sentencepiece]\n",
            "  Downloading transformers-4.9.1-py3-none-any.whl (2.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.6 MB 10.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.1.5)\n",
            "Collecting fsspec>=2021.05.0\n",
            "  Downloading fsspec-2021.7.0-py3-none-any.whl (118 kB)\n",
            "\u001b[K     |████████████████████████████████| 118 kB 16.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from datasets) (21.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from datasets) (4.6.1)\n",
            "Collecting tqdm>=4.42\n",
            "  Downloading tqdm-4.61.2-py2.py3-none-any.whl (76 kB)\n",
            "\u001b[K     |████████████████████████████████| 76 kB 3.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.19.5)\n",
            "Collecting xxhash\n",
            "  Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)\n",
            "\u001b[K     |████████████████████████████████| 243 kB 17.6 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub<0.1.0\n",
            "  Downloading huggingface_hub-0.0.15-py3-none-any.whl (43 kB)\n",
            "\u001b[K     |████████████████████████████████| 43 kB 1.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets) (0.70.12.2)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2.23.0)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.4)\n",
            "Requirement already satisfied: pyarrow!=4.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (3.0.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<0.1.0->datasets) (3.7.4.3)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->datasets) (2.4.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.5.0)\n",
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            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers[sentencepiece]) (2019.12.20)\n",
            "  Downloading huggingface_hub-0.0.12-py3-none-any.whl (37 kB)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 17.1 MB/s \n",
            "\u001b[?25hCollecting pyyaml>=5.1\n",
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            "\u001b[K     |████████████████████████████████| 636 kB 23.3 MB/s \n",
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            "  Downloading sacremoses-0.0.45-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 19.6 MB/s \n",
            "\u001b[?25hCollecting sentencepiece==0.1.91\n",
            "  Downloading sentencepiece-0.1.91-cp37-cp37m-manylinux1_x86_64.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 26.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from transformers[sentencepiece]) (3.17.3)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers[sentencepiece]) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers[sentencepiece]) (1.0.1)\n",
            "Installing collected packages: tqdm, tokenizers, sacremoses, pyyaml, huggingface-hub, xxhash, transformers, sentencepiece, fsspec, datasets\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.41.1\n",
            "    Uninstalling tqdm-4.41.1:\n",
            "      Successfully uninstalled tqdm-4.41.1\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed datasets-1.11.0 fsspec-2021.7.0 huggingface-hub-0.0.12 pyyaml-5.4.1 sacremoses-0.0.45 sentencepiece-0.1.91 tokenizers-0.10.3 tqdm-4.61.2 transformers-4.9.1 xxhash-2.0.2\n",
            "Collecting torch-xla==1.9\n",
            "  Downloading https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl (149.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 149.9 MB 39 kB/s \n",
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            "  Downloading accelerate-0.3.0-py3-none-any.whl (49 kB)\n",
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            "\u001b[?25hCollecting cloud-tpu-client==0.10\n",
            "  Downloading cloud_tpu_client-0.10-py3-none-any.whl (7.4 kB)\n",
            "Requirement already satisfied: oauth2client in /usr/local/lib/python3.7/dist-packages (from cloud-tpu-client==0.10) (4.1.3)\n",
            "Collecting google-api-python-client==1.8.0\n",
            "  Downloading google_api_python_client-1.8.0-py3-none-any.whl (57 kB)\n",
            "\u001b[K     |████████████████████████████████| 57 kB 3.6 MB/s \n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (3.0.4)\n",
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            "Requirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from accelerate) (1.9.0+cu102)\n",
            "Collecting pyaml>=20.4.0\n",
            "  Downloading pyaml-20.4.0-py2.py3-none-any.whl (17 kB)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from pyaml>=20.4.0->accelerate) (5.4.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4.0->accelerate) (3.7.4.3)\n",
            "Installing collected packages: pyaml, google-api-python-client, torch-xla, cloud-tpu-client, accelerate\n",
            "  Attempting uninstall: google-api-python-client\n",
            "    Found existing installation: google-api-python-client 1.12.8\n",
            "    Uninstalling google-api-python-client-1.12.8:\n",
            "      Successfully uninstalled google-api-python-client-1.12.8\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "earthengine-api 0.1.272 requires google-api-python-client<2,>=1.12.1, but you have google-api-python-client 1.8.0 which is incompatible.\u001b[0m\n",
            "Successfully installed accelerate-0.3.0 cloud-tpu-client-0.10 google-api-python-client-1.8.0 pyaml-20.4.0 torch-xla-1.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAl66AWG0B49",
        "colab": {
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          "height": 457,
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          ]
        },
        "outputId": "9848d2f1-f9c2-42a1-fa84-6d8e7229fabf"
      },
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "def tokenize_function(example):\n",
        "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2a14e4c0d3094c00ba30d8baa400f114",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50efe6b610914d6fb104d5e138125ee4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset glue/mrpc (download: 1.43 MiB, generated: 1.43 MiB, post-processed: Unknown size, total: 2.85 MiB) to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e32e627a01d54d69a1690c62f7720a9b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading: 0.00B [00:00, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "98c11eb8dfee4bee82e611df26b04245",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading: 0.00B [00:00, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "237f1bc0ca3540f3a01613b5526167dc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading: 0.00B [00:00, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6eaddcb9330c452f8e140934ce4b30de",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "137cb2ca4fb644418ac71dd8475ad551",
              "version_minor": 0,
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            },
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              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "57afe6c5fb3342fcb620d370f605e1a4",
              "version_minor": 0,
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            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fbec8e4360224adaa9b651e2ed3e3a72",
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              "model_id": "460a9f84d1bc4866958f9f3ef66777cd",
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aed191ae28784b138872506f166ab254",
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        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2439597ce1c9463ba3c0d7ab09d06117",
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        },
        {
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        },
        {
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0379707915741c3b728185dbd2b13d0",
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c0952a149ee543939078610b2175cedb",
              "version_minor": 0,
              "version_major": 2
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92WxeKMb0B4-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "097c8050-1114-4889-aa69-91c7acaf4ddb"
      },
      "source": [
        "tokenized_datasets = tokenized_datasets.remove_columns(\n",
        "    [\"sentence1\", \"sentence2\", \"idx\"]\n",
        ")\n",
        "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
        "tokenized_datasets.set_format(\"torch\")\n",
        "tokenized_datasets[\"train\"].column_names"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['attention_mask', 'input_ids', 'labels', 'token_type_ids']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSB511cp0B4-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99acdaa7-c6cd-4ce3-a609-0745618f262e"
      },
      "source": [
        "['attention_mask', 'input_ids', 'labels', 'token_type_ids']"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['attention_mask', 'input_ids', 'labels', 'token_type_ids']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tF_yschA0B4_"
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(\n",
        "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
        ")\n",
        "eval_dataloader = DataLoader(\n",
        "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
        ")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lawt8apz0B5A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04b5a334-b8b4-4585-cac1-9e24e2a1579f"
      },
      "source": [
        "for batch in train_dataloader:\n",
        "    break\n",
        "{k: v.shape for k, v in batch.items()}"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'attention_mask': torch.Size([8, 74]),\n",
              " 'input_ids': torch.Size([8, 74]),\n",
              " 'labels': torch.Size([8]),\n",
              " 'token_type_ids': torch.Size([8, 74])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuv3uSRA0B5B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "edc98c3e114f42e799cc9fee6154482e",
            "17d5648c2f9846668a1d06c99201c766",
            "6bc6922c35b74342878122e5c9832006",
            "f9dbc5f47a1346ba95cb7eaec3fe2445",
            "1d51b5223f474831b81228646d78f765",
            "ffa36f6669ed4782b02a8a26196a46df",
            "d316ade6eba1495eab9ecd4f87533d58",
            "205d5aeaccb5423b9674ee12655a280d",
            "45cf7d790837433291f031e4d61a3858",
            "6f0be223ec0e46c69900dcda1cf2db3a",
            "dd7dcd38bb9146828cb1e97fc6aedb6c"
          ]
        },
        "outputId": "9684c6c2-1347-483c-ceb7-04523d1d1257"
      },
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "edc98c3e114f42e799cc9fee6154482e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bczd_Wpb0B5C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a8b41b8-c7e4-4902-a2ac-530cb505ffad"
      },
      "source": [
        "outputs = model(**batch)\n",
        "print(outputs.loss, outputs.logits.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(0.3772, grad_fn=<NllLossBackward>) torch.Size([8, 2])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKkFVFGt0B5C"
      },
      "source": [
        "from transformers import AdamW\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1JRudbX0B5D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdce0b67-dd3d-483e-f007-3fb41f877e2b"
      },
      "source": [
        "from transformers import get_scheduler\n",
        "\n",
        "num_epochs = 3\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps\n",
        ")\n",
        "print(num_training_steps)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1377\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IU2dOWaA0B5D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf6be8a2-0cd5-4259-b454-3e750f68811f"
      },
      "source": [
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)\n",
        "device"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JfzRWno30B5E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "82cea34460a24d38940014fe9e199c9f",
            "6d67b371762c42f28b3bee6c04e2d2cf",
            "0f2366f1cd5d444ca6a6109bf091f5c2",
            "d65ec9e2d2bf4182a809a6e5779bbac3",
            "4753a5ec055c4922a88d316d63b87c23",
            "8a78c773f31746ec9a4b0389a9fd9572",
            "11a1253b668c436ab74cc771829a6140",
            "7e7d7798c0b34ee19f9adb6da7028e07",
            "694a49accd5d4cb9afb4de9448feca27",
            "83152c4f54db4158aacfbd323567d351",
            "4118b9e896b143819c44c756e0291e03"
          ]
        },
        "outputId": "0946d92a-969f-4ef2-9195-1f6172043ae3"
      },
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "# model.train()\n",
        "# for epoch in range(num_epochs):\n",
        "#     for batch in train_dataloader:\n",
        "#         batch = {k: v.to(device) for k, v in batch.items()}\n",
        "#         outputs = model(**batch)\n",
        "#         loss = outputs.loss\n",
        "#         loss.backward()\n",
        "        \n",
        "#         optimizer.step()\n",
        "#         lr_scheduler.step()\n",
        "#         optimizer.zero_grad()\n",
        "#         progress_bar.update(1)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "82cea34460a24d38940014fe9e199c9f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1377 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRbz1dLO0B5E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "5f7a954a9faf419e971c9f975e4869be",
            "bf95ad3eb573440e9c187adffa613d97",
            "95de58cff5dc47dda91a6174946aa015",
            "fc665763d872443cb78e7b48b28437d2",
            "abdb1575d48a4c5c8e2d532154deb653",
            "e6b22f11e947438a87ce92d081e6e0ae",
            "845a286628aa47829a6c620554554c1a",
            "af8418393a99473bb4a1e01dbab4a42d",
            "91a0f6d9236d46fc8a149b433fef4e3a",
            "3c346c0161a4495ea84eceed69fce84d",
            "38aa5cdd983147f79712dd8f5d7827fe"
          ]
        },
        "outputId": "2d1b7259-f404-47e5-9e82-86f35d44f494"
      },
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "metric= load_metric(\"glue\", \"mrpc\")\n",
        "model.eval()\n",
        "for batch in eval_dataloader:\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**batch)\n",
        "    \n",
        "    logits = outputs.logits\n",
        "    predictions = torch.argmax(logits, dim=-1)\n",
        "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "\n",
        "metric.compute()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f7a954a9faf419e971c9f975e4869be",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.86k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hxi_E1ZC0B5E"
      },
      "source": [
        "# from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
        "\n",
        "# model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
        "# optimizer = AdamW(model.parameters(), lr=3e-5)\n",
        "\n",
        "# device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "# model.to(device)\n",
        "\n",
        "# num_epochs = 3\n",
        "# num_training_steps = num_epochs * len(train_dataloader)\n",
        "# lr_scheduler = get_scheduler(\n",
        "#     \"linear\",\n",
        "#     optimizer=optimizer,\n",
        "#     num_warmup_steps=0,\n",
        "#     num_training_steps=num_training_steps\n",
        "# )\n",
        "\n",
        "# progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "# model.train()\n",
        "# for epoch in range(num_epochs):\n",
        "#     for batch in train_dataloader:\n",
        "#         batch = {k: v.to(device) for k, v in batch.items()}\n",
        "#         outputs = model(**batch)\n",
        "#         loss = outputs.loss\n",
        "#         loss.backward()\n",
        "        \n",
        "#         optimizer.step()\n",
        "#         lr_scheduler.step()\n",
        "#         optimizer.zero_grad()\n",
        "#         progress_bar.update(1)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDj6EWXT7pAO"
      },
      "source": [
        "#device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wa0hjAOg7kHv"
      },
      "source": [
        "# from accelerate import Accelerator\n",
        "# from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
        "\n",
        "# accelerator = Accelerator()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u84jAFJw8jsE"
      },
      "source": [
        ""
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-29BXa88XNn"
      },
      "source": [
        "# accelerator.device"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LTWY-w-0B5E"
      },
      "source": [
        "# from accelerate import Accelerator\n",
        "# from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
        "\n",
        "# accelerator = Accelerator()\n",
        "\n",
        "# model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
        "# optimizer = AdamW(model.parameters(), lr=3e-5)\n",
        "\n",
        "# train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
        "#     train_dataloader, eval_dataloader, model, optimizer\n",
        "# )\n",
        "\n",
        "# num_epochs = 3\n",
        "# num_training_steps = num_epochs * len(train_dl)\n",
        "# lr_scheduler = get_scheduler(\n",
        "#     \"linear\",\n",
        "#     optimizer=optimizer,\n",
        "#     num_warmup_steps=0,\n",
        "#     num_training_steps=num_training_steps\n",
        "# )\n",
        "\n",
        "# progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "# model.train()\n",
        "# for epoch in range(num_epochs):\n",
        "#     for batch in train_dl:\n",
        "#         outputs = model(**batch)\n",
        "#         loss = outputs.loss\n",
        "#         accelerator.backward(loss)\n",
        "        \n",
        "#         optimizer.step()\n",
        "#         lr_scheduler.step()\n",
        "#         optimizer.zero_grad()\n",
        "#         progress_bar.update(1)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3mvHUso_D66"
      },
      "source": [
        "def training_function():\n",
        "    from accelerate import Accelerator\n",
        "    from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
        "\n",
        "    accelerator = Accelerator()\n",
        "\n",
        "    model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
        "    optimizer = AdamW(model.parameters(), lr=3e-5)\n",
        "\n",
        "    train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
        "        train_dataloader, eval_dataloader, model, optimizer\n",
        "    )\n",
        "\n",
        "    num_epochs = 3\n",
        "    num_training_steps = num_epochs * len(train_dl)\n",
        "    lr_scheduler = get_scheduler(\n",
        "        \"linear\",\n",
        "        optimizer=optimizer,\n",
        "        num_warmup_steps=0,\n",
        "        num_training_steps=num_training_steps\n",
        "    )\n",
        "\n",
        "    progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "    model.train()\n",
        "    for epoch in range(num_epochs):\n",
        "        for batch in train_dl:\n",
        "            outputs = model(**batch)\n",
        "            loss = outputs.loss\n",
        "            accelerator.backward(loss)\n",
        "            \n",
        "            optimizer.step()\n",
        "            lr_scheduler.step()\n",
        "            optimizer.zero_grad()\n",
        "            progress_bar.update(1)\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQqRW4TL0B5F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "outputId": "fd7ff000-a8fb-4bc2-b873-e08217c3584e"
      },
      "source": [
        "from accelerate import notebook_launcher\n",
        "\n",
        "notebook_launcher(training_function)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Waiting for TPU to be start up with version pytorch-1.9...\n",
            "WARNING:root:Waiting for TPU to be start up with version pytorch-1.9...\n",
            "WARNING:root:TPU has started up successfully with version pytorch-1.9\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Launching a training on 8 TPU cores.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ProcessExitedException",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mProcessExitedException\u001b[0m                    Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-a91f3c0bb4fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0maccelerate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnotebook_launcher\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnotebook_launcher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_function\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/usr/local/lib/python3.7/dist-packages/accelerate/notebook_launcher.py\u001b[0m in \u001b[0;36mnotebook_launcher\u001b[0;34m(function, args, num_processes, use_fp16, use_port)\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0mlauncher\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPrepareForLaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistributed_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"TPU\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Launching a training on {num_processes} TPU cores.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m             \u001b[0mxmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspawn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlauncher\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnprocs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_processes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"fork\"\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     69\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m             \u001b[0;31m# No need for a distributed launch otherwise as it's either CPU or one GPU.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py\u001b[0m in \u001b[0;36mspawn\u001b[0;34m(fn, args, nprocs, join, daemon, start_method)\u001b[0m\n\u001b[1;32m    392\u001b[0m         \u001b[0mjoin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0mdaemon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdaemon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m         start_method=start_method)\n\u001b[0m\u001b[1;32m    395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py\u001b[0m in \u001b[0;36mstart_processes\u001b[0;34m(fn, args, nprocs, join, daemon, start_method)\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;31m# Loop on join until it returns True or raises an exception.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\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[0m\u001b[1;32m    189\u001b[0m         \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    134\u001b[0m                     \u001b[0merror_pid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfailed_process\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m                     \u001b[0mexit_code\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexitcode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m                     \u001b[0msignal_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m                 )\n\u001b[1;32m    138\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mProcessExitedException\u001b[0m: process 0 terminated with signal SIGSEGV"
          ]
        }
      ]
    }
  ]
}