examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_AlbertForQuestionAnswering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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_AlbertForQuestionAnswering.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import ONNX AlbertForQuestionAnswering 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.\n",
    "- `AlbertForQuestionAnswering` is only available since in `Spark NLP 5.1.1` and after. So please make sure you have upgraded to the latest Spark NLP release\n",
    "- You can import ALBERT models trained/fine-tuned for question answering via `AlbertForQuestionAnswering`. These models are usually under `Question Answering` category and have `albert` in their labels\n",
    "- Reference: [TFAlbertForQuestionAnswering](https://huggingface.co/transformers/model_doc/albert#transformers.TFAlbertForQuestionAnswering)\n",
    "- Some [example models](https://huggingface.co/models?filter=albert&pipeline_tag=question-answering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export and Save HuggingFace model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 `4.34.1`. This doesn't mean it won't work with the future releases, but we wanted you to know which versions have been tested successfully.\n",
    "- Albert uses SentencePiece, so we will have to install that as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25h\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 1.14.0 requires protobuf<4.21,>=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]==4.34.1 optimum sentencepiece tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 [twmkn9/albert-base-v2-squad2](https://huggingface.co/twmkn9/albert-base-v2-squad2) model from HuggingFace as an example and load it as a `ORTModelForQuestionAnswering`, representing an ONNX model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec15c2e6e2304fef92fa31fff51410af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/716 [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": "a99c78e01d8a4742bba2e5b680f2c52d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/46.7M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7140fb718b9e4c4dbb6c388f56afc466",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/39.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4dd41fcfb41942d9be4cf4cb9cbadf96",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/760k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29265726de524880ad2f214a1162979b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/156 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using framework PyTorch: 2.0.1+cu118\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 ORTModelForQuestionAnswering\n",
    "import tensorflow as tf\n",
    "\n",
    "MODEL_NAME = 'twmkn9/albert-base-v2-squad2'\n",
    "EXPORT_PATH = f\"onnx_models/{MODEL_NAME}\"\n",
    "\n",
    "ort_model = ORTModelForQuestionAnswering.from_pretrained(MODEL_NAME, export=True)\n",
    "\n",
    "# Save the ONNX model\n",
    "ort_model.save_pretrained(EXPORT_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look inside these two directories and see what we are dealing with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 46688\n",
      "-rw-r--r-- 1 root root      844 Sep  5 21:40 config.json\n",
      "-rw-r--r-- 1 root root 44758733 Sep  5 21:40 model.onnx\n",
      "-rw-r--r-- 1 root root      173 Sep  5 21:40 special_tokens_map.json\n",
      "-rw-r--r-- 1 root root   760289 Sep  5 21:40 spiece.model\n",
      "-rw-r--r-- 1 root root      538 Sep  5 21:40 tokenizer_config.json\n",
      "-rw-r--r-- 1 root root  2272267 Sep  5 21:40 tokenizer.json\n"
     ]
    }
   ],
   "source": [
    "!ls -l {EXPORT_PATH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As you can see, we need to move `spiece.model` from the tokenizer to `assets` folder which Spark NLP will look for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir {EXPORT_PATH}/assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mv {EXPORT_PATH}/spiece.model {EXPORT_PATH}/assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voila! We have our `spiece.model` inside assets directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "onnx_models/twmkn9/albert-base-v2-squad2:\n",
      "total 45948\n",
      "drwxr-xr-x 2 root root     4096 Sep  5 21:50 assets\n",
      "-rw-r--r-- 1 root root      844 Sep  5 21:40 config.json\n",
      "-rw-r--r-- 1 root root 44758733 Sep  5 21:40 model.onnx\n",
      "-rw-r--r-- 1 root root      173 Sep  5 21:40 special_tokens_map.json\n",
      "-rw-r--r-- 1 root root      538 Sep  5 21:40 tokenizer_config.json\n",
      "-rw-r--r-- 1 root root  2272267 Sep  5 21:40 tokenizer.json\n",
      "\n",
      "onnx_models/twmkn9/albert-base-v2-squad2/assets:\n",
      "total 744\n",
      "-rw-r--r-- 1 root root 760289 Sep  5 21:40 spiece.model\n"
     ]
    }
   ],
   "source": [
    "!ls -lR {EXPORT_PATH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import and Save AlbertForQuestionAnswering in Spark NLP\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing PySpark 3.2.3 and Spark NLP 5.1.0\n",
      "setup Colab for PySpark 3.2.3 and Spark NLP 5.1.0\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m281.5/281.5 MB\u001b[0m \u001b[31m1.3 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[32m531.2/531.2 kB\u001b[0m \u001b[31m26.1 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[31m16.5 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": {},
   "source": [
    "Let's start Spark with Spark NLP included via our simple `start()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apache Spark version: 3.2.3\n"
     ]
    }
   ],
   "source": [
    "import sparknlp\n",
    "# let's start Spark with Spark NLP\n",
    "spark = sparknlp.start()\n",
    "print(\"Apache Spark version: {}\".format(spark.version))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's use `loadSavedModel` functon in `AlbertForQuestionAnswering` which allows us to load TensorFlow model in SavedModel format\n",
    "- Most params can be set later when you are loading this model in `AlbertForQuestionAnswering` in runtime like `setMaxSentenceLength`, so don't worry what you are setting them now\n",
    "- `loadSavedModel` accepts two params, first is the path to the TF SavedModel. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\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.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sparknlp.annotator import *\n",
    "from sparknlp.base import *\n",
    "\n",
    "spanClassifier = AlbertForQuestionAnswering.loadSavedModel(\n",
    "     f\"{EXPORT_PATH}\",\n",
    "     spark\n",
    " )\\\n",
    "  .setInputCols([\"document_question\",'document_context'])\\\n",
    "  .setOutputCol(\"answer\")\\\n",
    "  .setCaseSensitive(False)\\\n",
    "  .setMaxSentenceLength(512)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [],
   "source": [
    "spanClassifier.write().overwrite().save(\"./{}_spark_nlp_onnx\".format(MODEL_NAME))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's clean up stuff we don't need anymore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm -rf {EXPORT_PATH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome 😎  !\n",
    "\n",
    "This is your AlbertForQuestionAnswering model from HuggingFace 🤗  loaded and saved by Spark NLP 🚀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 44468\n",
      "-rw-r--r-- 1 root root 44765729 Sep  5 22:02 albert_classification_onnx\n",
      "-rw-r--r-- 1 root root   760289 Sep  5 22:02 albert_spp\n",
      "drwxr-xr-x 2 root root     4096 Sep  5 22:02 metadata\n"
     ]
    }
   ],
   "source": [
    "! ls -l {MODEL_NAME}_spark_nlp_onnx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 AlbertForQuestionAnswering model in Spark NLP 🚀 pipeline!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+\n",
      "|result |\n",
      "+-------+\n",
      "|[clara]|\n",
      "+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "document_assembler = MultiDocumentAssembler() \\\n",
    "    .setInputCols([\"question\", \"context\"]) \\\n",
    "    .setOutputCols([\"document_question\", \"document_context\"])\n",
    "\n",
    "spanClassifier_loaded = AlbertForQuestionAnswering.load(\"./{}_spark_nlp_onnx\".format(MODEL_NAME))\\\n",
    "  .setInputCols([\"document_question\",'document_context'])\\\n",
    "  .setOutputCol(\"answer\")\n",
    "\n",
    "pipeline = Pipeline().setStages([\n",
    "    document_assembler,\n",
    "    spanClassifier_loaded\n",
    "])\n",
    "\n",
    "example = spark.createDataFrame([[\"What's my name?\", \"My name is Clara and I live in Berkeley.\"]]).toDF(\"question\", \"context\")\n",
    "result = pipeline.fit(example).transform(example)\n",
    "\n",
    "result.select(\"answer.result\").show(1, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! You can now go wild and use hundreds of `AlbertForQuestionAnswering` models from HuggingFace 🤗 in Spark NLP 🚀\n"
   ]
  }
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