HuggingFace_ONNX_in_Spark_NLP_BERT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTbQPzAQ4dca"
      },
      "source": [
        "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_BERT.ipynb)\n",
        "\n",
        "# Import ONNX BERT models from HuggingFace 🤗 into Spark NLP 🚀\n",
        "\n",
        "Let's keep in mind a few things before we start 😊\n",
        "\n",
        "- ONNX support was introduced in  `Spark NLP 5.0.0`, enabling high performance inference for models. Please make sure you have upgraded to the latest Spark NLP release.\n",
        "- You can import models for BERT from HuggingFace and they have to be in `Fill Mask` category. Meaning, you cannot use BERT models trained/fine-tuned on a specific task such as token/sequence classification."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vb1zKOIL4dcf"
      },
      "source": [
        "## Export and Save HuggingFace model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z89WWpj34dcg"
      },
      "source": [
        "- Let's install `transformers` package with the `onnx` extension and it's dependencies. You don't need `onnx` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n",
        "- We lock `transformers` on version `4.29.1`. This doesn't mean it won't work with the future releases, but we wanted you to know which versions have been tested successfully."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gsKxERUb4dch",
        "outputId": "699818b9-6769-4f4a-8281-c96c326a97d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m52.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for optimum (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\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",
            "tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\n",
            "tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q --upgrade transformers[onnx]==4.29.1 optimum"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1tk336R4dcm"
      },
      "source": [
        "- HuggingFace has an extension called Optimum which offers specialized model inference, including ONNX. We can use this to import and export ONNX models with `from_pretrained` and `save_pretrained`.\n",
        "- We'll use [bert-base-cased](https://huggingface.co/bert-base-cased) model from HuggingFace as an example and load it as a `ORTModelForFeatureExtraction`, representing an ONNX model.\n",
        "- In addition to the BERT model, we also need to save the `BertTokenizer`. This is the same for every model, these are assets (saved in `/assets`) needed for tokenization inside Spark NLP."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HbB1RfzJ4dcn",
        "outputId": "7c7ef2d0-5ad1-4740-b028-c8161998f268",
        "colab": {
          "referenced_widgets": [
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            "b8996fd1f1a3415d8d60422cf4bd9f72",
            "bfd976ee58154f1cb77f665377fa953b"
          ]
        }
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ada9173f56b841a39bac4509d7eee4b5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Framework not specified. Using pt to export to ONNX.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5d0c0491fb3745e3a75f52beb0f7bd83",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "20357cb4c69b4b0d8f5d4057e1ec3603",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "197c4900589f4e32a8927241af41ff50",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43ee3d5c0ebc47e4b26b64cd299e6192",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using framework PyTorch: 2.0.1+cu118\n",
            "Overriding 1 configuration item(s)\n",
            "\t- use_cache -> False\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============= Diagnostic Run torch.onnx.export version 2.0.1+cu118 =============\n",
            "verbose: False, log level: Level.ERROR\n",
            "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from optimum.onnxruntime import ORTModelForFeatureExtraction\n",
        "\n",
        "MODEL_NAME = \"bert-base-cased\"\n",
        "EXPORT_PATH = f\"onnx_models/{MODEL_NAME}\"\n",
        "\n",
        "ort_model = ORTModelForFeatureExtraction.from_pretrained(MODEL_NAME, export=True)\n",
        "\n",
        "# Save the ONNX model\n",
        "ort_model.save_pretrained(EXPORT_PATH)\n",
        "\n",
        "# Create directory for assets and move the tokenizer files.\n",
        "# A separate folder is needed for Spark NLP.\n",
        "!mkdir {EXPORT_PATH}/assets\n",
        "!mv {EXPORT_PATH}/vocab.txt {EXPORT_PATH}/assets/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASg7wdWo4dco"
      },
      "source": [
        "Let's have a look inside these two directories and see what we are dealing with:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "arFBE5ck4dcp",
        "outputId": "9e599be9-64f1-4663-b9ba-d909d80e1979"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 421028\n",
            "drwxr-xr-x 2 root root      4096 Jul 11 09:15 assets\n",
            "-rw-r--r-- 1 root root       634 Jul 11 09:15 config.json\n",
            "-rw-r--r-- 1 root root 431123644 Jul 11 09:15 model.onnx\n"
          ]
        }
      ],
      "source": [
        "!ls -l {EXPORT_PATH}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eetIFmW24dcq",
        "outputId": "06d9645b-ca1e-4f56-8edc-456cd8556399"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 876\n",
            "-rw-r--r-- 1 root root    125 Jul 11 09:15 special_tokens_map.json\n",
            "-rw-r--r-- 1 root root    315 Jul 11 09:15 tokenizer_config.json\n",
            "-rw-r--r-- 1 root root 668923 Jul 11 09:15 tokenizer.json\n",
            "-rw-r--r-- 1 root root 213450 Jul 11 09:15 vocab.txt\n"
          ]
        }
      ],
      "source": [
        "!ls -l {EXPORT_PATH}/assets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u57Mq86G4dcr"
      },
      "source": [
        "## Import and Save BERT in Spark NLP\n",
        "\n",
        "- Let's install and setup Spark NLP in Google Colab\n",
        "- This part is pretty easy via our simple script"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D00Iv9Gj4dcs",
        "outputId": "5d233cdc-f3ef-451d-cbab-68fe4213a156"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Installing PySpark 3.2.3 and Spark NLP 5.0.0\n",
            "setup Colab for PySpark 3.2.3 and Spark NLP 5.0.0\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m281.5/281.5 MB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m498.7/498.7 kB\u001b[0m \u001b[31m37.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 kB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "! wget -q http://setup.johnsnowlabs.com/colab.sh -O - | bash"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AERNecfG4dct"
      },
      "source": [
        "Let's start Spark with Spark NLP included via our simple `start()` function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zq7OtICm4dcu"
      },
      "outputs": [],
      "source": [
        "import sparknlp\n",
        "# let's start Spark with Spark NLP\n",
        "spark = sparknlp.start()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ISLbZ8aS4dcu"
      },
      "source": [
        "- Let's use `loadSavedModel` functon in `BertEmbeddings` which allows us to load the ONNX model\n",
        "- Most params will be set automatically. They can also be set later after loading the model in `BertEmbeddings` during runtime, so don't worry about setting them now\n",
        "- `loadSavedModel` accepts two params, first is the path to the exported model. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\n",
        "- `setStorageRef` is very important. When you are training a task like NER or any Text Classification, we use this reference to bound the trained model to this specific embeddings so you won't load a different embeddings by mistake and see terrible results 😊\n",
        "- It's up to you what you put in `setStorageRef` but it cannot be changed later on. We usually use the name of the model to be clear, but you can get creative if you want!\n",
        "- The `dimension` param is is purely cosmetic and won't change anything. It's mostly for you to know later via `.getDimension` what is the dimension of your model. So set this accordingly.\n",
        "- NOTE: `loadSavedModel` accepts local paths in addition to distributed file systems such as `HDFS`, `S3`, `DBFS`, etc. This feature was introduced in Spark NLP 4.2.2 release. Keep in mind the best and recommended way to move/share/reuse Spark NLP models is to use `write.save` so you can use `.load()` from any file systems natively.st and recommended way to move/share/reuse Spark NLP models is to use `write.save` so you can use `.load()` from any file systems natively.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LOm2iZ0K4dcv"
      },
      "outputs": [],
      "source": [
        "from sparknlp.annotator import *\n",
        "\n",
        "# All these params should be identical to the original ONNX model\n",
        "bert = BertEmbeddings.loadSavedModel(f\"{EXPORT_PATH}\", spark)\\\n",
        "    .setInputCols([\"document\",'token'])\\\n",
        "    .setOutputCol(\"bert\")\\\n",
        "    .setCaseSensitive(True)\\\n",
        "    .setDimension(768)\\\n",
        "    .setStorageRef('bert_base_cased')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z_Q6HICN4dcv"
      },
      "source": [
        "- Let's save it on disk so it is easier to be moved around and also be used later via `.load` function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lF2YnsPi4dcw"
      },
      "outputs": [],
      "source": [
        "bert.write().overwrite().save(f\"{MODEL_NAME}_spark_nlp\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOl8uTvD4dcx"
      },
      "source": [
        "Let's clean up stuff we don't need anymore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dUZWOgSI4dcx"
      },
      "outputs": [],
      "source": [
        "!rm -rf {EXPORT_PATH}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHZVgh2I4dcy"
      },
      "source": [
        "Awesome  😎 !\n",
        "\n",
        "This is your ONNX BERT model from HuggingFace 🤗  loaded and saved by Spark NLP 🚀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j2i8Tmcp4dcz",
        "outputId": "eba6ed4e-e50b-4eb8-f9d0-3273723016d5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 421092\n",
            "-rw-r--r-- 1 root root 431189561 Jul 11 09:18 bert_onnx\n",
            "drwxr-xr-x 3 root root      4096 Jul 11 09:17 fields\n",
            "drwxr-xr-x 2 root root      4096 Jul 11 09:17 metadata\n"
          ]
        }
      ],
      "source": [
        "! ls -l {MODEL_NAME}_spark_nlp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uG9pq-LE4dcz"
      },
      "source": [
        "Now let's see how we can use it on other machines, clusters, or any place you wish to use your new and shiny BERT model 😊"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nABjfjZY4dc1"
      },
      "outputs": [],
      "source": [
        "import sparknlp\n",
        "\n",
        "from sparknlp.base import *\n",
        "from sparknlp.annotator import *\n",
        "\n",
        "document_assembler = DocumentAssembler()\\\n",
        "    .setInputCol(\"text\")\\\n",
        "    .setOutputCol(\"document\")\n",
        "\n",
        "tokenizer = Tokenizer()\\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"token\")\n",
        "\n",
        "bert_loaded = BertEmbeddings.load(f\"{MODEL_NAME}_spark_nlp\")\\\n",
        "    .setInputCols([\"document\",'token'])\\\n",
        "    .setOutputCol(\"bert\")\\\n",
        "\n",
        "pipeline = Pipeline(\n",
        "    stages = [\n",
        "        document_assembler,\n",
        "        tokenizer,\n",
        "        bert_loaded\n",
        "  ])\n",
        "\n",
        "data = spark.createDataFrame([['William Henry Gates III (born October 28, 1955) is an American business magnate, software developer, investor,and philanthropist.']]).toDF(\"text\")\n",
        "model = pipeline.fit(data)\n",
        "result = model.transform(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qdjh3gEk4dc4",
        "outputId": "1a6d6335-3826-441e-bbce-cc0aaf938193"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+--------------------+\n",
            "|          embeddings|\n",
            "+--------------------+\n",
            "|[0.43426436, -0.3...|\n",
            "|[-0.033402592, -0...|\n",
            "|[0.38291216, 0.11...|\n",
            "|[-0.11996313, 0.2...|\n",
            "|[-0.4832554, 0.05...|\n",
            "|[-0.17415513, 0.2...|\n",
            "|[0.030410081, -0....|\n",
            "|[-0.09456871, -1....|\n",
            "|[0.20999376, 0.27...|\n",
            "|[-0.6175975, -0.1...|\n",
            "|[0.26205128, 0.31...|\n",
            "|[0.071797684, 0.3...|\n",
            "|[0.11466834, 0.16...|\n",
            "|[0.112313405, 0.2...|\n",
            "|[0.97112143, 0.13...|\n",
            "|[0.6206643, -0.10...|\n",
            "|[0.21066168, 0.42...|\n",
            "|[0.451869, 0.2410...|\n",
            "|[0.3347211, -0.16...|\n",
            "|[0.10000777, -0.3...|\n",
            "+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "result.selectExpr(\"explode(bert.embeddings) as embeddings\").show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYSIajTl4dc5"
      },
      "source": [
        "That's it! You can now go wild and use hundreds of BERT models from HuggingFace 🤗 in Spark NLP 🚀\n"
      ]
    }
  ],
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