question_answering_portuguese_with_BetterTransformer.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# QA in Portuguese with BetterTransformer"
      ],
      "metadata": {
        "id": "y9w92EpHdy2-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "(by [Pierre Guillou](https://www.linkedin.com/in/pierreguillou/) | [blog post](https://medium.com/@pierre_guillou/ia-empresas-diminua-o-tempo-de-infer%C3%AAncia-de-modelos-transformer-com-bettertransformer-584a5a7702c8) | 22/11/2022) "
      ],
      "metadata": {
        "id": "uwKdXP3KUAMu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Configuration"
      ],
      "metadata": {
        "id": "ktzUmAyRd1vS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vQ6XRxirILoO"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install transformers>=4.24.0 \n",
        "!pip install optimum>=1.5.0 \n",
        "!pip install gradio>=3.10.1 \n",
        "!pip install matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "print(torch.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XunVCnFlu4-r",
        "outputId": "73802500-243b-400f-dbe2-27807efa2951"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.12.1+cu113\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*In* case you are not using at least PyTorch 1.13 (check with `torch.__version__`) or running in Colab, install the latest version of PyTorch:"
      ],
      "metadata": {
        "id": "xubN3f30u_Ac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip3 install -q --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116"
      ],
      "metadata": {
        "id": "mFCn7Jl2umdq"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qCGXQmWYZJz",
        "outputId": "711fd7f9-cfc8-4048-bb59-0b62102454ac"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Nov 22 12:48:08 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   67C    P0    30W /  70W |   4464MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download models"
      ],
      "metadata": {
        "id": "mI3G4Mktd8Ok"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "import time\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import matplotlib as plt\n",
        "\n",
        "# device\n",
        "import torch\n",
        "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "# models\n",
        "model_name_bb = \"pierreguillou/bert-base-cased-squad-v1.1-portuguese\"\n",
        "model_name_bl = \"pierreguillou/bert-large-cased-squad-v1.1-portuguese\"\n",
        "\n",
        "# load models\n",
        "from transformers import pipeline\n",
        "qa_bb = pipeline(\"question-answering\", model_name_bb, device=device)\n",
        "\n",
        "from optimum.pipelines import pipeline\n",
        "qa_bb_better = pipeline(\"question-answering\", model_name_bb, accelerator=\"bettertransformer\", device=device)\n",
        "\n",
        "from transformers import pipeline\n",
        "qa_bl = pipeline(\"question-answering\", model_name_bl, device=device)\n",
        "\n",
        "from optimum.pipelines import pipeline\n",
        "qa_bl_better = pipeline(\"question-answering\", model_name_bl, accelerator=\"bettertransformer\", device=device)"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predictions function"
      ],
      "metadata": {
        "id": "i0EiDoCfeM1U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function to get results\n",
        "def get_answer(context, question):\n",
        "\n",
        "  # get predictions\n",
        "  start = time.perf_counter()\n",
        "  answer_bl = qa_bl(question=question, context=context)\n",
        "  end = time.perf_counter()\n",
        "  diff_bl = round(end - start, 2)\n",
        "  answer_bl[\"time (s)\"] = diff_bl\n",
        "  del answer_bl[\"start\"]\n",
        "  del answer_bl[\"end\"]\n",
        "\n",
        "  start = time.perf_counter()\n",
        "  answer_bl_better = qa_bl_better(question=question, context=context)\n",
        "  end = time.perf_counter()\n",
        "  diff_bl_better = round(end - start, 2)\n",
        "  answer_bl_better[\"time (s)\"] = diff_bl_better\n",
        "  del answer_bl_better[\"start\"]\n",
        "  del answer_bl_better[\"end\"]\n",
        "\n",
        "  start = time.perf_counter()\n",
        "  answer_bb = qa_bb(question=question, context=context)\n",
        "  end = time.perf_counter()\n",
        "  diff_bb = round(end - start, 2)\n",
        "  answer_bb[\"time (s)\"] = diff_bb\n",
        "  del answer_bb[\"start\"]\n",
        "  del answer_bb[\"end\"]\n",
        "\n",
        "  start = time.perf_counter()\n",
        "  answer_bb_better = qa_bb_better(question=question, context=context)\n",
        "  end = time.perf_counter()\n",
        "  diff_bb_better = round(end - start, 2)\n",
        "  answer_bb_better[\"time (s)\"] = diff_bb_better\n",
        "  del answer_bb_better[\"start\"]\n",
        "  del answer_bb_better[\"end\"]\n",
        "\n",
        "  answer = dict()\n",
        "  answer[\"BERT large\"] = answer_bl\n",
        "  answer[\"BERT large (BetterTransformer)\"] = answer_bl_better\n",
        "  answer[\"BERT base\"] = answer_bb\n",
        "  answer[\"BERT base (BetterTransformer)\"] = answer_bb_better\n",
        "\n",
        "  # get image of prediction times\n",
        "  df = pd.DataFrame.from_dict({\"Method\":[\"BERT base (BetterTransformer)\", \"BERT base\", \"BERT large (BetterTransformer)\", \"BERT large\"], \n",
        "                               \"Time (seconds)\":  [answer[\"BERT base (BetterTransformer)\"][\"time (s)\"], answer[\"BERT base\"][\"time (s)\"], answer[\"BERT large (BetterTransformer)\"][\"time (s)\"], answer[\"BERT large\"][\"time (s)\"]]})\n",
        "  ax = df.plot.barh(x='Method', title=f'Prediction times on {str(device).replace(\"cuda:0\", \"GPU\").replace(\"cpu\", \"CPU\")}')\n",
        "  ax.figure.savefig(\"img.png\", bbox_inches='tight')\n",
        "  image = Image.open('img.png')\n",
        "  !rm img.png\n",
        "\n",
        "  return image, answer"
      ],
      "metadata": {
        "id": "r80XP_7OIR_A"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gradio App"
      ],
      "metadata": {
        "id": "-kl0-grBeGFC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "title = \"QA in Portuguese with BetterTransformer (this App runs on \" + str(device).replace(\"cuda:0\", \"GPU\").replace(\"cpu\", \"CPU\") + \")\"\n",
        "description = '<p>(20/11/2022) Forneça seu próprio parágrafo e faça perguntas sobre o texto. Quão bem os modelos respondem?<br />(este aplicativo usa os modelos <a href=\"https://huggingface.co/pierreguillou/bert-base-cased-squad-v1.1-portuguese\">pierreguillou/bert-base-cased-squad-v1.1-portuguese</a> and <a href=\"https://huggingface.co/pierreguillou/bert-large-cased-squad-v1.1-portuguese\">pierreguillou/bert-large-cased-squad-v1.1-portuguese</a> and their versions <a href=\"https://huggingface.co/docs/optimum/bettertransformer/overview\">BetterTransformer</a>)</p>'\n",
        "examples = [\n",
        "    [\"Dom Pedro II foi o segundo e último monarca do Império do Brasil, reinando por mais de 58 anos.\", \"Quem foi Dom Pedro II?\"],\n",
        "    [\"A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano.\", \"Quando começou a pandemia de Covid-19 no mundo?\"],\n",
        "    [\"A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano.\", \"Onde começou a pandemia de Covid-19?\"]\n",
        "    ]\n",
        "\n",
        "demo = gr.Interface(\n",
        "    fn=get_answer,\n",
        "    inputs=[\n",
        "        gr.Textbox(lines=7, label=\"Context\"), \n",
        "        gr.Textbox(lines=2, label=\"Question\")\n",
        "        ],\n",
        "    outputs=[\n",
        "        gr.Image(label=\"Prediction times\", type=\"pil\"),\n",
        "        gr.JSON(label=\"Results\"),\n",
        "        ],\n",
        "    title=title,\n",
        "    description=description,\n",
        "    examples=examples,\n",
        "    allow_flagging=\"never\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    demo.launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "H-UQ1BI4eDDC",
        "outputId": "81266bfd-59f2-463c-d73b-1dff526ed7cd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7860, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# END"
      ],
      "metadata": {
        "id": "iWznc2v9eEfb"
      }
    }
  ]
}