finetune/TensorFlow/notebooks/Tensorflow-BERT-AzureML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow Pretrained BERT on Azure Machine Learning\n",
    "This notebook contains an end-to-end walkthrough of using Azure Machine Learning Service to run [Google's TensorFlow repository for the BERT model](https://github.com/google-research/bert).\n",
    "\n",
    "You will find the following contents:\n",
    "- Download pretrained TensorFlow BERT checkpoint and GLUE dataset on the remote compute and store them in Azure storage\n",
    "- Speep-up fine-tuning BERT for GLUE dataset on AzureML GPU clusters\n",
    "- Further fine-tune BERT wtih AzureML hyperparameter optimizer "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
    "* If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SDK version: 1.0.18\n"
     ]
    }
   ],
   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize workspace\n",
    "\n",
    "To create or access an Azure ML Workspace, you will need to import the AML library and the following information:\n",
    "* A name for your workspace\n",
    "* Your subscription id\n",
    "* The resource group name\n",
    "\n",
    "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step or create a new one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: Falling back to use azure cli login credentials.\n",
      "If you run your code in unattended mode, i.e., where you can't give a user input, then we recommend to use ServicePrincipalAuthentication or MsiAuthentication.\n",
      "Please refer to aka.ms/aml-notebook-auth for different authentication mechanisms in azureml-sdk.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote the config file BERT_AML_config.json to: F:\\AzureML-BERT\\TensorFlow\\aml_config\\BERT_AML_config.json\n",
      "Workspace configuration succeeded. Skip the workspace creation steps below\n"
     ]
    }
   ],
   "source": [
    "import azureml.core\n",
    "import os\n",
    "from azureml.core import Workspace\n",
    "\n",
    "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"\")\n",
    "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"\")\n",
    "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"\")\n",
    "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"\")\n",
    "\n",
    "\n",
    "try:\n",
    "    ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
    "    # write the details of the workspace to a configuration file to the notebook library\n",
    "    ws.write_config(file_name=\"BERT_AML_config.json\")\n",
    "    print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n",
    "except:\n",
    "    print(\"Workspace not accessible. Change your parameters or create a new workspace below\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Model and GLUE dataset on the remote compute\n",
    "\n",
    "We will download the model and store it in an Azure Blob container. We will also download the [GLUE data](https://gluebenchmark.com/tasks) by running the [script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e) and unpack it to the same Azure Blob container.\n",
    "\n",
    "All of the above steps will be done in a remote AzureML cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a project directory\n",
    "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "project_folder = './bert'\n",
    "os.makedirs(project_folder, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a local clone of the original repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system('git clone https://github.com/google-research/bert.git')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have prepared a python script [download_model_and_dataset.py](download_model_and_dataset.py) to do the following tasks:\n",
    "- Download [the pre-trained models released by Google](https://github.com/google-research/bert#pre-trained-models) to local directory\n",
    "- Dump model and its metadata into a mounted Azure Blob container\n",
    "- Run the [script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e) to download the [GLUE data](https://gluebenchmark.com/tasks) in the mounted Azure Blob container\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./bert\\\\download_model_and_dataset.py'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import shutil\n",
    "shutil.copy('../download_model_and_dataset.py', project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define AzureML datastore to collect converted model file\n",
    "\n",
    "To make data accessible for remote training, AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data to Azure Storage, and interact with it from your remote compute targets.\n",
    "\n",
    "Each workspace is associated with a default Azure Blob datastore named `'workspaceblobstore'`. In this work, we use this default datastore to collect the converted model file and the GLUE training dataset ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Datastore\n",
    "ds = Datastore(ws, 'workspaceblobstore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an experiment\n",
    "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this tutorial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Experiment\n",
    "\n",
    "experiment_name = 'BERT_TF'\n",
    "experiment = Experiment(ws, name=experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Tensorflow estimator\n",
    "The AML SDK's Tensorflow estimator enables you to easily submit Tensorflow training jobs for both single-node and distributed runs. For more information on the Tensorflow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow). The entry script `download_model_and_dataset.py` takes model name variable:\n",
    "\n",
    "- BERT-Base, Uncased: 12-layer: `uncased_L-12_H-768_A-12`\n",
    "- BERT-Large, Uncased: `uncased_L-24_H-1024_A-16`\n",
    "- BERT-Base, Cased: `cased_L-12_H-768_A-12`\n",
    "- BERT-Base, Multilingual: `multilingual_L-12_H-768_A-12`\n",
    "- BERT-Base, Chinese: `chinese_L-12_H-768_A-12`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.dnn import TensorFlow\n",
    "\n",
    "script_params = {\n",
    "    '--bert_model_name': 'uncased_L-12_H-768_A-12',\n",
    "    '--model_dump_path': ds.path('BERT_model/uncased_L-12_H-768_A-12').as_mount(),\n",
    "    '--glue_data_path': ds.path('glue_data_path').as_mount()\n",
    "}\n",
    "\n",
    "estimator = TensorFlow(source_directory=project_folder,\n",
    "                    vm_size='STANDARD_D2_v2',\n",
    "                    script_params=script_params,\n",
    "                    entry_script='download_model_and_dataset.py',\n",
    "                    node_count=1,\n",
    "                    use_gpu=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code defines a CPU cluster with `vm_size` of `STANDARD_D2_V2`. We do not need GPU cluster for data movement tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit and Monitor your run\n",
    "Run your experiment by submitting your estimator object. The widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab844660ea2a49feadf3795d4d71ac37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': True, 'log_level': 'INFO', 's…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run = experiment.submit(estimator)\n",
    "from azureml.widgets import RunDetails\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tuning with BERT and GLUE dataset\n",
    "As our `BERT` model and `GLUE` dataset are ready in Azure storage, we can start to fine-tune the model on a single node using a GPU. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Tensorflow estimator for fine-tuning\n",
    "Let us create a new Tensorflow estimator to run the fine-tuning script `run_classifier.py`, that is already provided at [the original repository](https://github.com/google-research/bert/blob/master/run_classifier.py). Please refer [here](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) for more detail about the script. A `STANDARD_NC6` vm_size is used, which has 1 GPU.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.dnn import TensorFlow\n",
    "\n",
    "ft_estimator = TensorFlow(source_directory=project_folder,\n",
    "                    vm_size='STANDARD_NC6',\n",
    "                    script_params = {\n",
    "                          '--task_name': 'MRPC',\n",
    "                          '--do_train' : True,\n",
    "                          '--do_eval': True,\n",
    "                          '--data_dir': ds.path('glue_data_path/MRPC/').as_mount(),\n",
    "                          '--vocab_file': ds.path('BERT_model/uncased_L-12_H-768_A-12/vocab.txt').as_mount(),\n",
    "                          '--bert_config_file': ds.path('BERT_model/uncased_L-12_H-768_A-12/bert_config.json').as_mount(),\n",
    "                          '--init_checkpoint': ds.path('BERT_model/uncased_L-12_H-768_A-12/bert_model.ckpt').as_mount(),\n",
    "                          '--max_seq_length': 128,\n",
    "                          '--train_batch_size': 32,\n",
    "                          '--learning_rate': 2e-5,\n",
    "                          '--num_train_epochs': 3.0,\n",
    "                          '--output_dir': './outputs'\n",
    "                    },\n",
    "                    entry_script='run_classifier.py',\n",
    "                    use_gpu=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit and Monitor your run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88610c37dd7148d1802a8f67d9cecdb9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': True, 'log_level': 'INFO', 's…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run = experiment.submit(ft_estimator)\n",
    "from azureml.widgets import RunDetails\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-Tuning BERT with Hyperparameter Tuning\n",
    "\n",
    "We would also like to optimize our hyperparameter, `learning rate`, using Azure Machine Learning's hyperparameter tuning capabilities.\n",
    "\n",
    "To use AML's tracking and metrics capabilities, we need to add a small amount of AzureML code inside the training script.\n",
    "\n",
    "In `run_classifier.py`, we will log some metrics to our AML run. To do so, we will access the AML run object within the script:\n",
    "```Python\n",
    "from azureml.core.run import Run\n",
    "run = Run.get_context()\n",
    "```\n",
    "Further within `run_classifier.py`, we log the learning rate, and the epoch training and eval loss the model achieves:\n",
    "```Python\n",
    "run.log('lr', np.float(args.learning_rate))\n",
    "\n",
    "run.log('train_mean_loss', mean_loss)\n",
    "run.log('eval_mean_loss', mean_loss)\n",
    "run.log('train_example_loss', mean_loss)\n",
    "run.log('eval_example_loss', mean_loss)\n",
    "```\n",
    "These run metrics will become particularly important when we begin hyperparameter tuning our model in the \"Tune model hyperparameters\" section.\n",
    "\n",
    "Let's first copy the modified [run_classifier.py](./run_classifier.py) into our local project directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./bert\\\\run_classifier.py'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shutil.copy('run_classifier.py', project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start a hyperparameter sweep\n",
    "First, we will define the hyperparameter space to sweep over. In this example we will use random sampling to try different configuration sets of hyperparameter to maximize our primary metric, the mean training loss (`train_mean_loss`).  We specify a `loguniform` distribution for the learning rate parameter since we want to evenly space the samples over a range that varies by a few orders of magnitude, ~1e-4 to ~1e-6.\n",
    "\n",
    "Then, we specify the early termination policy to use to early terminate poorly performing runs. Here we use the `BanditPolicy`, which will terminate any run that doesn't fall within the slack factor of our primary evaluation metric. In this tutorial, we will apply this policy every epoch (since we report our `train_mean_loss` metric every epoch and `evaluation_interval=1`). \n",
    "Refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-tune-hyperparameters#specify-an-early-termination-policy) for more information on the BanditPolicy and other policies available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.hyperdrive import *\n",
    "\n",
    "param_sampling = RandomParameterSampling( {\n",
    "        'learning_rate': loguniform(-14, -9),\n",
    "    }\n",
    ")\n",
    "\n",
    "policy = BanditPolicy(delay_evaluation=25, evaluation_interval=1, slack_amount=0.10)\n",
    "\n",
    "hyperdrive_run_config = HyperDriveRunConfig(estimator=ft_estimator,\n",
    "                                            hyperparameter_sampling=param_sampling, \n",
    "                                            primary_metric_name='train_mean_loss',\n",
    "                                            primary_metric_goal=PrimaryMetricGoal.MINIMIZE,\n",
    "                                            policy=policy,\n",
    "                                            max_total_runs=8,\n",
    "                                            max_concurrent_runs=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, lauch the hyperparameter tuning job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperdrive_run = experiment.submit(hyperdrive_run_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Monitor Hyper Parameter Tuning runs\n",
    "We can monitor the progress of the runs with the following Jupyter widget. You will get something like below in Jupyter Notebook:"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1d4f2a17da7487ab5096eb387a64f69",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': True, 'log_level': 'INFO',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "\n",
    "RunDetails(hyperdrive_run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find and register the best model\n",
    "Once all the runs complete, we can find the run that produced the model with the highest accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
    "best_run_metrics = best_run.get_metrics()\n",
    "print(best_run)\n",
    "print('Best Run is:\\n  Validation accuracy: {0:.5f} \\n  Validation loss: {1:.5f} \\n  Learning rate: {2:.8f}'.format(\n",
    "        best_run_metrics['eval_accuracy'],\n",
    "        best_run_metrics['loss'],\n",
    "        best_run_metrics['lr']\n",
    "     ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can compare the resulting optimal `learning_rate` with the value suggested by the [original implementation hyper-parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks): 2e-5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "minxia"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "msauthor": "minxia"
 },
 "nbformat": 4,
 "nbformat_minor": 2
}