{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b16631a1",
   "metadata": {},
   "source": [
    "# Hugging Face and Sagemaker: fine-tuning a seq2seq model\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a08e66aa",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b7423739",
   "metadata": {},
   "source": [
    "In this script, we use the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library to fine-tune a seq2seq model, Pegasus, for medical text summarization tasks. This script was developed and tested in SageMaker Studio.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "11e80c86",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "65958c43",
   "metadata": {},
   "source": [
    "[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus) is a sequence to sequence (encoder-decoder) model that was introduced by Google AI in 2020. It is specifically designed for abstractive text summarization tasks and has shown impressive results in various benchmark datasets."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "976aaa34",
   "metadata": {},
   "source": [
    "## Setup "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "19de5fc9",
   "metadata": {},
   "source": [
    "[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus) is a transformer-based model that was introduced by Google AI in 2020. It is specifically designed for abstractive text summarization tasks and has shown impressive results in various benchmark datasets."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bcbbfa60",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e85e6feb",
   "metadata": {},
   "source": [
    "Install the required dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bd11e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install transformers --quiet\n",
    "%pip install accelerate --quiet\n",
    "%pip install datasets --quiet\n",
    "%pip install -U sagemaker --quiet\n",
    "%pip install s3fs --quiet"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f408437c",
   "metadata": {},
   "source": [
    "### Variables and hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22c4271b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "# vars\n",
    "model_checkpoint = 'google/pegasus-xsum'\n",
    "bucket_name = 'YOUR_BUCKET_NAME'\n",
    "train_data_path = 'data/train.csv'\n",
    "test_data_path = 'data/test.csv'\n",
    "artifact_path = 'training_artifacts/%s/' % datetime.today().strftime('%Y-%m-%d') \n",
    "\n",
    "# tokenizer\n",
    "max_target_length = 32\n",
    "max_input_length = 512\n",
    "ds_col_full = \"note\"\n",
    "ds_col_summ = \"summary\"\n",
    "\n",
    "# training\n",
    "batch_size = 1\n",
    "num_train_epochs = 5\n",
    "learning_rate = 1e-3\n",
    "optimizer_name = 'Adam' # must be a supported algorithm from https://pytorch.org/docs/stable/optim.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0d6d733",
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "s3 = boto3.client('s3')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4da6ac75",
   "metadata": {},
   "source": [
    "### Getting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61a2b643",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "dataset = load_dataset(\"csv\", data_files={\n",
    "    \"train\": f's3://{bucket_name}/{train_data_path}', \n",
    "    \"validation\": f's3://{bucket_name}/{test_data_path}'\n",
    "})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fed13d64",
   "metadata": {},
   "source": [
    "### Tokenizer\n",
    "Prepares data for the model by mapping text into numerical inputs called tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "50819eb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
    "\n",
    "def preprocess_function(examples):\n",
    "    model_inputs = tokenizer(\n",
    "        examples[ds_col_full],\n",
    "        max_length=max_input_length,\n",
    "        truncation=True,\n",
    "        padding='max_length'\n",
    "    )\n",
    "    labels = tokenizer(\n",
    "        examples[ds_col_summ], max_length=max_target_length, truncation=True, padding='max_length'\n",
    "    )\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
    "    return model_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d66a827",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_datasets = dataset.map(preprocess_function, batched=True)\n",
    "\n",
    "tokenized_datasets.set_format(\"torch\")\n",
    "\n",
    "tokenized_datasets = tokenized_datasets.remove_columns(\n",
    "    dataset[\"train\"].column_names\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6089084",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForSeq2SeqLM\n",
    "\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e68f4516",
   "metadata": {},
   "source": [
    "### Data Collator\n",
    "Pads data during batching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7bb15232",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DataCollatorForSeq2Seq\n",
    "\n",
    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ddfa5186",
   "metadata": {},
   "source": [
    "### Optimizer\n",
    "The optimizer maintains training state and update parameters based on training loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd688874",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Hardcode the optimizer, replaced by following code block\n",
    "\n",
    "#from torch.optim import Adam\n",
    "\n",
    "#optimizer = Adam(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b11d192b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dynamically select optimizer based on input var\n",
    "\n",
    "from importlib import import_module\n",
    "\n",
    "module = import_module('torch.optim')\n",
    "opt_fnc = getattr(module, optimizer_name)\n",
    "\n",
    "optimizer = opt_fnc(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "54032d51",
   "metadata": {},
   "source": [
    "### Accelerator\n",
    "The accelerator enables distributed training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5c63b83c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from accelerate import Accelerator\n",
    "\n",
    "accelerator = Accelerator()\n",
    "model, optimizer = accelerator.prepare(\n",
    "    model, optimizer\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "aa9d9f82",
   "metadata": {},
   "source": [
    "### Learning rate scheduler\n",
    "Manages adjustments to the learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e0e6c26b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import get_scheduler\n",
    "\n",
    "num_update_steps_per_epoch = len(tokenized_datasets[\"train\"]) / batch_size\n",
    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
    "\n",
    "lr_scheduler = get_scheduler(\n",
    "    \"linear\",\n",
    "    optimizer=optimizer,\n",
    "    num_warmup_steps=0,\n",
    "    num_training_steps=num_training_steps\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "95b87bcf-cb89-4e5a-bd23-67fae6cdf88f",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e317e00-9f89-43d8-abd4-997f48766480",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import Seq2SeqTrainer\n",
    "from transformers import Seq2SeqTrainingArguments\n",
    "\n",
    "training_args = Seq2SeqTrainingArguments(\n",
    "    output_dir=\"output/\",\n",
    "    save_total_limit=1,\n",
    "    num_train_epochs=num_train_epochs,\n",
    "    per_device_train_batch_size=batch_size,\n",
    "    per_device_eval_batch_size=batch_size,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    logging_dir=\"output/\",\n",
    "    load_best_model_at_end=True,\n",
    "    disable_tqdm=True,\n",
    "    logging_first_step=True,\n",
    "    logging_steps=1,\n",
    "    save_strategy=\"epoch\",\n",
    "    predict_with_generate=True\n",
    ")\n",
    "\n",
    "trainer = Seq2SeqTrainer(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_datasets[\"train\"],\n",
    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
    "    data_collator=data_collator,\n",
    "    optimizers=(optimizer, lr_scheduler)\n",
    ")\n",
    "    \n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bdd1778e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unwrapped_model = accelerator.unwrap_model(trainer.model)\n",
    "unwrapped_model.save_pretrained('model_dir', save_function=accelerator.save)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed04831c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenizer.save_pretrained('model_dir')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a5acc717",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open(\"model_dir/special_tokens_map.json\", \"rb\") as f:\n",
    "    s3.upload_fileobj(f, bucket_name, artifact_path + \"special_tokens_map.json\")\n",
    "with open(\"model_dir/tokenizer_config.json\", \"rb\") as f:\n",
    "    s3.upload_fileobj(f, bucket_name, artifact_path + \"tokenizer_config.json\")\n",
    "with open(\"model_dir/tokenizer.json\", \"rb\") as f:\n",
    "    s3.upload_fileobj(f, bucket_name, artifact_path + \"tokenizer.json\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c332b0a8",
   "metadata": {},
   "source": [
    "### Zip and save the model to S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6971934d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!cd model_dir/ && tar -czvf model.tar.gz *\n",
    "!mv model_dir/model.tar.gz ./"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "57fe54ed",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open(\"model.tar.gz\", \"rb\") as f:\n",
    "    s3.upload_fileobj(f, bucket_name, artifact_path + \"model/model.tar.gz\")"
   ]
  }
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