tutorials/building_text_classifier.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Building text classifier with Differential Privacy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this tutorial we will train a text classifier with Differential Privacy by taking a model pre-trained on public text data and fine-tuning it for a different task.\n",
        "\n",
        "When training a model with differential privacy, we almost always face a trade-off between model size and accuracy on the task. The exact details depend on the problem, but a rule of thumb is that the fewer parameters the model has, the easier it is to get a good performance with DP.\n",
        "\n",
        "Most state-of-the-art NLP models are quite deep and large (e.g. [BERT-base](https://github.com/google-research/bert) has over 100M parameters), which makes task of training text model on a private datasets rather challenging.\n",
        "\n",
        "One way of addressing this problem is to divide the training process into two stages. First, we will pre-train the model on a public dataset, exposing the model to generic text data. Assuming that the generic text data is public, we will not be using differential privacy at this step. Then, we freeze most of the layers, leaving only a few upper layers to be trained on the private dataset using DP-SGD. This way we can get the best of both worlds - we have a deep and powerful text understanding model, while only training a small number of parameters with differentially private algorithm.\n",
        "\n",
        "In this tutorial we will take the pre-trained [BERT-base](https://github.com/google-research/bert) model and fine-tune it to recognize textual entailment on the [SNLI](https://nlp.stanford.edu/projects/snli/) dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "First, we need to download the dataset (we'll user Stanford NLP mirror)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "STANFORD_SNLI_URL = \"https://nlp.stanford.edu/projects/snli/snli_1.0.zip\"\n",
        "DATA_DIR = \"data\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and extracting ...\n",
            "Completed!\n"
          ]
        }
      ],
      "source": [
        "import zipfile\n",
        "import urllib.request\n",
        "import os\n",
        "\n",
        "def download_and_extract(dataset_url, data_dir):\n",
        "    print(\"Downloading and extracting ...\")\n",
        "    filename = \"snli.zip\"\n",
        "    urllib.request.urlretrieve(dataset_url, filename)\n",
        "    with zipfile.ZipFile(filename) as zip_ref:\n",
        "        zip_ref.extractall(data_dir)\n",
        "    os.remove(filename)\n",
        "    print(\"Completed!\")\n",
        "\n",
        "download_and_extract(STANFORD_SNLI_URL, DATA_DIR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The dataset comes in two formats (`tsv` and `json`) and has already been split into train/dev/test. Let’s verify that’s the case."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['.DS_Store',\n",
              " 'Icon\\r',\n",
              " 'README.txt',\n",
              " 'snli_1.0_dev.jsonl',\n",
              " 'snli_1.0_dev.txt',\n",
              " 'snli_1.0_test.jsonl',\n",
              " 'snli_1.0_test.txt',\n",
              " 'snli_1.0_train.jsonl',\n",
              " 'snli_1.0_train.txt']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "snli_folder = os.path.join(DATA_DIR, \"snli_1.0\")\n",
        "os.listdir(snli_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's now take a look inside. [SNLI dataset](https://nlp.stanford.edu/projects/snli/) provides ample syntactic metadata, but we'll only use raw input text. Therefore, the only fields we're interested in are **sentence1** (premise), **sentence2** (hypothesis) and **gold_label** (label chosen by the majority of annotators).\n",
        "\n",
        "Label defines the relation between premise and hypothesis: either *contradiction*, *neutral* or *entailment*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.dataresource+json": {
              "data": [
                {
                  "gold_label": "neutral",
                  "index": 0,
                  "sentence1": "A person on a horse jumps over a broken down airplane.",
                  "sentence2": "A person is training his horse for a competition."
                },
                {
                  "gold_label": "contradiction",
                  "index": 1,
                  "sentence1": "A person on a horse jumps over a broken down airplane.",
                  "sentence2": "A person is at a diner, ordering an omelette."
                },
                {
                  "gold_label": "entailment",
                  "index": 2,
                  "sentence1": "A person on a horse jumps over a broken down airplane.",
                  "sentence2": "A person is outdoors, on a horse."
                },
                {
                  "gold_label": "neutral",
                  "index": 3,
                  "sentence1": "Children smiling and waving at camera",
                  "sentence2": "They are smiling at their parents"
                },
                {
                  "gold_label": "entailment",
                  "index": 4,
                  "sentence1": "Children smiling and waving at camera",
                  "sentence2": "There are children present"
                }
              ],
              "schema": {
                "fields": [
                  {
                    "name": "index",
                    "type": "integer"
                  },
                  {
                    "name": "sentence1",
                    "type": "string"
                  },
                  {
                    "name": "sentence2",
                    "type": "string"
                  },
                  {
                    "name": "gold_label",
                    "type": "string"
                  }
                ],
                "pandas_version": "0.20.0",
                "primaryKey": [
                  "index"
                ]
              }
            },
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sentence1</th>\n",
              "      <th>sentence2</th>\n",
              "      <th>gold_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A person on a horse jumps over a broken down a...</td>\n",
              "      <td>A person is training his horse for a competition.</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A person on a horse jumps over a broken down a...</td>\n",
              "      <td>A person is at a diner, ordering an omelette.</td>\n",
              "      <td>contradiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A person on a horse jumps over a broken down a...</td>\n",
              "      <td>A person is outdoors, on a horse.</td>\n",
              "      <td>entailment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Children smiling and waving at camera</td>\n",
              "      <td>They are smiling at their parents</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Children smiling and waving at camera</td>\n",
              "      <td>There are children present</td>\n",
              "      <td>entailment</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           sentence1  \\\n",
              "0  A person on a horse jumps over a broken down a...   \n",
              "1  A person on a horse jumps over a broken down a...   \n",
              "2  A person on a horse jumps over a broken down a...   \n",
              "3              Children smiling and waving at camera   \n",
              "4              Children smiling and waving at camera   \n",
              "\n",
              "                                           sentence2     gold_label  \n",
              "0  A person is training his horse for a competition.        neutral  \n",
              "1      A person is at a diner, ordering an omelette.  contradiction  \n",
              "2                  A person is outdoors, on a horse.     entailment  \n",
              "3                  They are smiling at their parents        neutral  \n",
              "4                         There are children present     entailment  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "train_path =  os.path.join(snli_folder, \"snli_1.0_train.txt\")\n",
        "dev_path = os.path.join(snli_folder, \"snli_1.0_dev.txt\")\n",
        "\n",
        "df_train = pd.read_csv(train_path, sep='\\t')\n",
        "df_test = pd.read_csv(dev_path, sep='\\t')\n",
        "\n",
        "df_train[['sentence1', 'sentence2', 'gold_label']][:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "BERT (Bidirectional Encoder Representations from Transformers) is state of the art approach to various NLP tasks. It uses a Transformer architecture and relies heavily on the concept of pre-training. \n",
        "\n",
        "We'll use a pre-trained BERT-base model, provided in huggingface [transformers](https://github.com/huggingface/transformers) repo.\n",
        "It gives us a pytorch implementation for the classic BERT architecture, as well as a tokenizer and weights pre-trained on a public English corpus (Wikipedia).\n",
        "\n",
        "Please follow these [installation instrucitons](https://github.com/huggingface/transformers#installation) before proceeding."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from transformers import BertConfig, BertTokenizer, BertForSequenceClassification\n",
        "\n",
        "model_name = \"bert-base-cased\"\n",
        "config = BertConfig.from_pretrained(\n",
        "    model_name,\n",
        "    num_labels=3,\n",
        ")\n",
        "tokenizer = BertTokenizer.from_pretrained(\n",
        "    \"bert-base-cased\",\n",
        "    do_lower_case=False,\n",
        ")\n",
        "model = BertForSequenceClassification.from_pretrained(\n",
        "    \"bert-base-cased\",\n",
        "    config=config,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The model has the following structure. It uses a combination of word, positional and token *embeddings* to create a sequence representation, then passes the data through 12 *transformer encoders* and finally uses a *linear classifier* to produce the final label.\n",
        "As the model is already pre-trained and we only plan to fine-tune few upper layers, we want to freeze all layers, except for the last encoder and above (`BertPooler` and `Classifier`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Image\n",
        "Image(filename='img/BERT.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total parameters count: 108312579\n",
            "Trainable parameters count: 7680771\n"
          ]
        }
      ],
      "source": [
        "trainable_layers = [model.bert.encoder.layer[-1], model.bert.pooler, model.classifier]\n",
        "total_params = 0\n",
        "trainable_params = 0\n",
        "\n",
        "for p in model.parameters():\n",
        "        p.requires_grad = False\n",
        "        total_params += p.numel()\n",
        "\n",
        "for layer in trainable_layers:\n",
        "    for p in layer.parameters():\n",
        "        p.requires_grad = True\n",
        "        trainable_params += p.numel()\n",
        "\n",
        "print(f\"Total parameters count: {total_params}\") # ~108M\n",
        "print(f\"Trainable parameters count: {trainable_params}\") # ~7M"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Thus, by using pre-trained model we reduce the number of trainable params from over 100 millions to just above 7.5 millions. This will help both performance and convergence with added noise."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prepare the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Before we begin training, we need to preprocess the data and convert it to the format our model expects. \n",
        "\n",
        "(Note: it'll take 5-10 minutes to run on a laptop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "LABEL_LIST = ['contradiction', 'entailment', 'neutral']\n",
        "MAX_SEQ_LENGHT = 128\n",
        "\n",
        "import torch\n",
        "import transformers\n",
        "from torch.utils.data import TensorDataset\n",
        "from transformers.data.processors.utils import InputExample\n",
        "from transformers.data.processors.glue import glue_convert_examples_to_features\n",
        "\n",
        "\n",
        "def _create_examples(df, set_type):\n",
        "    \"\"\" Convert raw dataframe to a list of InputExample. Filter malformed examples\n",
        "    \"\"\"\n",
        "    examples = []\n",
        "    for index, row in df.iterrows():\n",
        "        if row['gold_label'] not in LABEL_LIST:\n",
        "            continue\n",
        "        if not isinstance(row['sentence1'], str) or not isinstance(row['sentence2'], str):\n",
        "            continue\n",
        "            \n",
        "        guid = f\"{index}-{set_type}\"\n",
        "        examples.append(\n",
        "            InputExample(guid=guid, text_a=row['sentence1'], text_b=row['sentence2'], label=row['gold_label']))\n",
        "    return examples\n",
        "\n",
        "def _df_to_features(df, set_type):\n",
        "    \"\"\" Pre-process text. This method will:\n",
        "    1) tokenize inputs\n",
        "    2) cut or pad each sequence to MAX_SEQ_LENGHT\n",
        "    3) convert tokens into ids\n",
        "    \n",
        "    The output will contain:\n",
        "    `input_ids` - padded token ids sequence\n",
        "    `attention mask` - mask indicating padded tokens\n",
        "    `token_type_ids` - mask indicating the split between premise and hypothesis\n",
        "    `label` - label\n",
        "    \"\"\"\n",
        "    examples = _create_examples(df, set_type)\n",
        "    \n",
        "    #backward compatibility with older transformers versions\n",
        "    legacy_kwards = {}\n",
        "    from packaging import version\n",
        "    if version.parse(transformers.__version__) < version.parse(\"2.9.0\"):\n",
        "        legacy_kwards = {\n",
        "            \"pad_on_left\": False,\n",
        "            \"pad_token\": tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],\n",
        "            \"pad_token_segment_id\": 0,\n",
        "        }\n",
        "    \n",
        "    return glue_convert_examples_to_features(\n",
        "        examples=examples,\n",
        "        tokenizer=tokenizer,\n",
        "        label_list=LABEL_LIST,\n",
        "        max_length=MAX_SEQ_LENGHT,\n",
        "        output_mode=\"classification\",\n",
        "        **legacy_kwards,\n",
        "    )\n",
        "\n",
        "def _features_to_dataset(features):\n",
        "    \"\"\" Convert features from `_df_to_features` into a single dataset\n",
        "    \"\"\"\n",
        "    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n",
        "    all_attention_mask = torch.tensor(\n",
        "        [f.attention_mask for f in features], dtype=torch.long\n",
        "    )\n",
        "    all_token_type_ids = torch.tensor(\n",
        "        [f.token_type_ids for f in features], dtype=torch.long\n",
        "    )\n",
        "    all_labels = torch.tensor([f.label for f in features], dtype=torch.long)\n",
        "    dataset = TensorDataset(\n",
        "        all_input_ids, all_attention_mask, all_token_type_ids, all_labels\n",
        "    )\n",
        "\n",
        "    return dataset\n",
        "\n",
        "train_features = _df_to_features(df_train, \"train\")\n",
        "test_features = _df_to_features(df_test, \"test\")\n",
        "\n",
        "train_dataset = _features_to_dataset(train_features)\n",
        "test_dataset = _features_to_dataset(test_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Choosing batch size\n",
        "\n",
        "Let's talk about batch sizes for a bit.\n",
        "\n",
        "In addition to all the considerations you normally take into account when choosing batch size, training model with DP adds another one - privacy cost. \n",
        "\n",
        "Because of the threat model we assume and the way we add noise to the gradients, larger batch sizes (to a certain extent) generally help convergence. We add the same amount of noise to each gradient update (scaled to the norm of one sample in the batch) regardless of the batch size. What this means is that as the batch size increases, the relative amount of noise added decreases. while preserving the same epsilon guarantee. \n",
        "\n",
        "You should, however, keep in mind that increasing batch size has its price in terms of epsilon, which grows at `O(sqrt(batch_size))` as we train (therefore larger batches make it grow faster). The good strategy here is to experiment with multiple combinations of `batch_size` and `noise_multiplier` to find the one that provides best possible quality at acceptable privacy guarantee.\n",
        "\n",
        "There's another side to this - memory. Opacus computes and stores *per sample* gradients, so for every normal gradient, Opacus will store `n=batch_size` per-sample gradients on each step, thus increasing the memory footprint by at least `O(batch_size)`. In reality, however, the peak memory requirement is `O(batch_size^2)` compared to non-private model. This is because some intermediate steps in per sample gradient computation involve operations on two matrices, each with batch_size as one of the dimensions.\n",
        "\n",
        "The good news is, we can pick the most appropriate batch size, regardless of memory constrains. Opacus has built-in support for *virtual* batches. Using it we can separate physical steps (gradient computation) and logical steps (noise addition and parameter updates): use larger batches for training, while keeping memory footprint low. Below we will specify two constants:\n",
        "\n",
        "- `BATCH_SIZE` defines the maximum batch size we can afford from a memory standpoint, and only affects computation speed\n",
        "- `VIRTUAL_BATCH_SIZE`, on the other hand, is equivalent to normal batch_size in the non-private setting, and will affect convergence and privacy guarantee.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "BATCH_SIZE = 8\n",
        "VIRTUAL_BATCH_SIZE = 32"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
        "\n",
        "train_sampler = RandomSampler(train_dataset)\n",
        "train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=BATCH_SIZE)\n",
        "\n",
        "test_sampler = SequentialSampler(test_dataset)\n",
        "test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "# Move the model to appropriate device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = model.to(device)\n",
        "\n",
        "# Define optimizer\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, eps=1e-8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next we will define and attach PrivacyEngine. There are two parameters you need to consider here:\n",
        "\n",
        "- `noise_multiplier`. It defines the trade-off between privacy and accuracy. Adding more noise will provide stronger privacy guarantees, but will also hurt model quality.\n",
        "- `max_grad_norm`. Defines the maximum magnitude of L2 norms to which we clip per sample gradients. There is a bit of tug of war with this threshold: on the one hand, a low threshold means that we will clip many gradients, hurting convergence, so we might be tempted to raise it. However, recall that we add noise with `std=noise_multiplier * max_grad_norm` so we will pay for the increased threshold with more noise. In most cases you can rely on the model being quite resilient to clipping (after the first few iterations your model will tend to adjust so that its gradients stay below the clipping threshold), so you can often just keep the default value (`=1.0`) and focus on tuning `batch_size` and `noise_multiplier` instead. That being said, sometimes clipping hurts the model so it may be worth experimenting with different clipping thresholds, like we are doing in this tutorial.\n",
        "\n",
        "These two parameters define the scale of the noise we add to gradients: the noise will be sampled from a Gaussian distribution with `std=noise_multiplier * max_grad_norm`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "from opacus import PrivacyEngine\n",
        "\n",
        "ALPHAS = [1 + x / 10.0 for x in range(1, 100)] + list(range(12, 64))\n",
        "NOISE_MULTIPLIER = 0.4\n",
        "MAX_GRAD_NORM = 0.1\n",
        "\n",
        "privacy_engine = PrivacyEngine(\n",
        "    module=model,\n",
        "    batch_size=VIRTUAL_BATCH_SIZE,\n",
        "    sample_size=len(train_dataset),\n",
        "    alphas=ALPHAS,\n",
        "    noise_multiplier=NOISE_MULTIPLIER,\n",
        "    max_grad_norm=MAX_GRAD_NORM,\n",
        ")\n",
        "privacy_engine.attach(optimizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let’s first define the evaluation cycle."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "def accuracy(preds, labels):\n",
        "    return (preds == labels).mean()\n",
        "\n",
        "# define evaluation cycle\n",
        "def evaluate(model):    \n",
        "    model.eval()\n",
        "\n",
        "    loss_arr = []\n",
        "    accuracy_arr = []\n",
        "    \n",
        "    for batch in test_dataloader:\n",
        "        batch = tuple(t.to(device) for t in batch)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            inputs = {'input_ids':      batch[0],\n",
        "                      'attention_mask': batch[1],\n",
        "                      'token_type_ids': batch[2],\n",
        "                      'labels':         batch[3]}\n",
        "\n",
        "            outputs = model(**inputs)\n",
        "            loss, logits = outputs[:2]\n",
        "            \n",
        "            preds = np.argmax(logits.detach().cpu().numpy(), axis=1)\n",
        "            labels = inputs['labels'].detach().cpu().numpy()\n",
        "            \n",
        "            loss_arr.append(loss.item())\n",
        "            accuracy_arr.append(accuracy(preds, labels))\n",
        "    \n",
        "    model.train()\n",
        "    return np.mean(loss_arr), np.mean(accuracy_arr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we specify the training parameters and run the training loop for three epochs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "EPOCHS = 3\n",
        "LOGGING_INTERVAL = 1000 # once every how many steps we run evaluation cycle and report metrics\n",
        "DELTA = 1 / len(train_dataloader) # Parameter for privacy accounting. Probability of not uploding privacy guarantees\n",
        "\n",
        "\n",
        "assert VIRTUAL_BATCH_SIZE % BATCH_SIZE == 0 # VIRTUAL_BATCH_SIZE should be divisible by BATCH_SIZE\n",
        "virtual_batch_rate = VIRTUAL_BATCH_SIZE / BATCH_SIZE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for epoch in range(1, EPOCHS+1):\n",
        "    losses = []\n",
        "    for step, batch in enumerate(tqdm(train_dataloader)):\n",
        "        \n",
        "        batch = tuple(t.to(device) for t in batch)\n",
        "        inputs = {'input_ids':      batch[0],\n",
        "                  'attention_mask': batch[1],\n",
        "                  'token_type_ids': batch[2],\n",
        "                  'labels':         batch[3]}\n",
        "\n",
        "        outputs = model(**inputs) # output = loss, logits, hidden_states, attentions\n",
        "        \n",
        "        loss = outputs[0]\n",
        "        loss.backward()\n",
        "        \n",
        "        losses.append(loss.item())\n",
        "\n",
        "        # We process small batches of size BATCH_SIZE, \n",
        "        # until they're accumulated to a batch of size VIRTUAL_BATCH_SIZE.\n",
        "        # Only then we make a real `.step()` and update model weights\n",
        "        if (step + 1) % virtual_batch_rate == 0 or step == len(train_dataloader) - 1:\n",
        "            optimizer.step()\n",
        "        else:\n",
        "            optimizer.virtual_step()\n",
        "\n",
        "        model.zero_grad()\n",
        "\n",
        "        if step > 0 and step % LOGGING_INTERVAL == 0:\n",
        "            train_loss = np.mean(losses)\n",
        "            eps, alpha = optimizer.privacy_engine.get_privacy_spent(DELTA)\n",
        "\n",
        "            eval_loss, eval_accuracy = evaluate(model)\n",
        "\n",
        "            print(\n",
        "                f\"Epoch: {epoch} | \"\n",
        "                f\"Step: {step} | \"\n",
        "                f\"Train loss: {train_loss:.3f} | \"\n",
        "                f\"Eval loss: {eval_loss:.3f} | \"\n",
        "                f\"Eval accuracy: {eval_accuracy:.3f} | \"\n",
        "                f\"ɛ: {eps:.2f} (α: {alpha})\"\n",
        "            )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For the test accuracy, after training for three epochs you should expect something close to the results below.\n",
        "\n",
        "You can see that we can achieve quite strong privacy guarantee at epsilon=7.5 with a moderate accuracy cost of 11 percentage points compared to non-private model trained in a similar setting (upper layers only) and 16 points compared to best results we were able to achieve using the same architecture.\n",
        "\n",
        "*NB: When not specified, DP-SGD is trained with upper layers only*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true
      },
      "source": [
        "| Model | Noise multiplier | Batch size | Accuracy | Epsilon |\n",
        "| --- | --- | --- | --- | --- |\n",
        "| no DP, train full model | N/A | 32 | 90.1% | N/A |\n",
        "| no DP, train upper layers only | N/A | 32 | 85.4% | N/A |\n",
        "| DP-SGD | 1.0 | 32 | 70.5% | 0.7 |\n",
        "| **DP-SGD (this tutorial)** | **0.4** | **32** | **74.3%** | **7.5** |\n",
        "| DP-SGD | 0.3 | 32 | 75.8% | 20.7 |\n",
        "| DP-SGD | 0.1 | 32 | 78.3% | 2865 |\n",
        "| DP-SGD | 0.4 | 8 | 67.3% | 5.9 |"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "python3",
      "language": "python",
      "name": "python3"
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