05_finetune/05_prepare_dialogsum_prompt_dataset_huggingface.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Feature Transformation in this Notebook\n",
    "\n",
    "In this notebook, we convert raw text into tokenized inputs which are ready to be ingested by a HuggingFace training script. It is important to separate this tokenizing and prompt creation step of the process from training because it will allow you to tailor the compute which is most efficient for each step of the process. For instance a low-cost CPU processor is generally the best bet for the preparation section of the workflow while a higher cost GPU instance is best for model training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Tested on ml.m5.2xlarge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name='1'></a>\n",
    "## Set up Kernel and Required Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --disable-pip-version-check \\\n",
    "    torch==2.0.1 \\\n",
    "    transformers==4.34.1 \\\n",
    "    datasets==2.12.0 \\\n",
    "    accelerate==0.23.0 \\\n",
    "    evaluate==0.4.0 \\\n",
    "    py7zr==0.20.4 \\\n",
    "    sentencepiece==0.1.99 \\\n",
    "    rouge_score==0.1.2 \\\n",
    "    loralib==0.1.1 \\\n",
    "    peft==0.4.0 \\\n",
    "    trl==0.7.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "from datasets import load_dataset, DatasetDict\n",
    "import os\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensure the Base Dataset is Downloaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from datasets import concatenate_datasets\n",
    "dataset = load_dataset(\"knkarthick/dialogsum\")\n",
    "dataset = concatenate_datasets([dataset['train'], dataset['test'], dataset['validation']])\n",
    "!mkdir data-summarization\n",
    "dataset = dataset.train_test_split(0.5, seed=1234)\n",
    "dataset['test'].to_csv('./data-summarization/dialogsum-1.csv', index=False)\n",
    "dataset['train'].to_csv('./data-summarization/dialogsum-2.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Tokenizer and HuggingFace Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_checkpoint='google/flan-t5-base'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/data-summarization to /root/.cache/huggingface/datasets/csv/data-summarization-137942e411c15b41/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1...\n"
     ]
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "model_id": "43703f6c9aa24308881cc2452860c51c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/data-summarization-137942e411c15b41/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1. Subsequent calls will reuse this data.\n"
     ]
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    {
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       "model_id": "a2da391699934e41808bcc2f64820526",
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    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
       "        num_rows: 14460\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
    "dataset = load_dataset('./data-summarization/')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore an Example Prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt:\n",
      "--------------------------\n",
      "Summarize the following conversation.\n",
      "\n",
      "#Person1#: If we employ you, what starting salary would you expect?\n",
      "#Person2#: I'd like to start at 3000 yuan a month.\n",
      "#Person1#: I think your background and experience are worth the compensation.\n",
      "#Person2#: Does it include bonuses?\n",
      "#Person1#: No, there are annual bonuses, one week paid vacation a year, and health insurance.\n",
      "#Person2#: Very good.\n",
      "\n",
      "Summary:\n",
      "--------------------------\n",
      "Baseline human summary : #Person1# agrees #Person2#'s starting monthly salary would be 3000 yuan and tells #Person2# about other benefits.\n"
     ]
    }
   ],
   "source": [
    "idx = 0\n",
    "diag = dataset['train'][idx]['dialogue']\n",
    "baseline_human_summary = dataset['train'][idx]['summary']\n",
    "\n",
    "prompt = f'Summarize the following conversation.\\n\\n{diag}\\n\\nSummary:'\n",
    "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
    "\n",
    "print(f'Prompt:\\n--------------------------\\n{prompt}\\n--------------------------')\n",
    "print(f'Baseline human summary : {baseline_human_summary}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenize the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def tokenize_function(example):\n",
    "    prompt = 'Summarize the following conversation.\\n\\n'\n",
    "    end_prompt = '\\n\\nSummary: '\n",
    "    inp = [prompt + i + end_prompt for i in example[\"dialogue\"]]\n",
    "    example['input_ids'] = tokenizer(inp, padding=\"max_length\", truncation=True, return_tensors=\"pt\").input_ids\n",
    "    example['labels'] = tokenizer(example[\"summary\"], padding=\"max_length\", truncation=True, return_tensors=\"pt\").input_ids\n",
    "    return example\n",
    "\n",
    "tokenized_datasets = dataset.map(tokenize_function, batched=True)\n",
    "tokenized_datasets = tokenized_datasets.remove_columns(['id', 'topic', 'dialogue', 'summary',])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrap the preprocessing into a repeatable function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def tokenize_function(example):\n",
    "    prompt = 'Summarize the following conversation.\\n\\n'\n",
    "    end_prompt = '\\n\\nSummary: '\n",
    "    inp = [prompt + i + end_prompt for i in example[\"dialogue\"]]\n",
    "    example['input_ids'] = tokenizer(inp, padding=\"max_length\", truncation=True, return_tensors=\"pt\").input_ids\n",
    "    example['labels'] = tokenizer(example[\"summary\"], padding=\"max_length\", truncation=True, return_tensors=\"pt\").input_ids\n",
    "    return example\n",
    "\n",
    "def transform_dataset(input_data,\n",
    "                      output_data,\n",
    "                      huggingface_model_name,\n",
    "                      train_split_percentage,\n",
    "                      test_split_percentage,\n",
    "                      validation_split_percentage,\n",
    "                      ):\n",
    "\n",
    "    # load in the original dataset\n",
    "    dataset = load_dataset(input_data)\n",
    "    print(f'Dataset loaded from path: {input_data}\\n{dataset}')\n",
    "    \n",
    "    # Load the tokenizer\n",
    "    print(f'Loading the tokenizer for the model {huggingface_model_name}')\n",
    "    tokenizer = AutoTokenizer.from_pretrained(huggingface_model_name)\n",
    "    \n",
    "    # make train test validation split\n",
    "    train_testvalid = dataset['train'].train_test_split(1 - train_split_percentage, seed=1234)\n",
    "    test_valid = train_testvalid['test'].train_test_split(test_split_percentage / (validation_split_percentage + test_split_percentage), seed=1234)\n",
    "    train_test_valid_dataset = DatasetDict(\n",
    "        {\n",
    "            'train': train_testvalid['train'],\n",
    "            'test': test_valid['test'],\n",
    "            'validation': test_valid['train']\n",
    "        }\n",
    "    )\n",
    "    print(f'Dataset after splitting:\\n{train_test_valid_dataset}')\n",
    "    \n",
    "    # tokenize the dataset\n",
    "    print(f'Tokenizing the dataset...')\n",
    "    tokenized_datasets = train_test_valid_dataset.map(tokenize_function, batched=True)\n",
    "    tokenized_datasets = tokenized_datasets.remove_columns(['id', 'topic', 'dialogue', 'summary'])\n",
    "    print(f'Tokenizing complete!')\n",
    "    \n",
    "    # create directory for drop\n",
    "    os.makedirs(f'{output_data}/train/', exist_ok=True)\n",
    "    os.makedirs(f'{output_data}/test/', exist_ok=True)\n",
    "    os.makedirs(f'{output_data}/validation/', exist_ok=True)\n",
    "    file_root = str(int(time.time()*1000))\n",
    "    \n",
    "    # save the dataset to disk\n",
    "    print(f'Writing the dataset to {output_data}')\n",
    "    tokenized_datasets['train'].to_parquet(f'./{output_data}/train/{file_root}.parquet')\n",
    "    tokenized_datasets['test'].to_parquet(f'./{output_data}/test/{file_root}.parquet')\n",
    "    tokenized_datasets['validation'].to_parquet(f'./{output_data}/validation/{file_root}.parquet')\n",
    "    print('Preprocessing complete!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def process(args):\n",
    "\n",
    "    print(f\"Listing contents of {args.input_data}\")\n",
    "    dirs_input = os.listdir(args.input_data)\n",
    "    for file in dirs_input:\n",
    "        print(file)\n",
    "\n",
    "    transform_dataset(input_data=args.input_data, #'./data-summarization/',\n",
    "                      output_data=args.output_data, #'./data-summarization-processed/',\n",
    "                      huggingface_model_name=args.model_checkpoint, #model_checkpoint,\n",
    "                      train_split_percentage=args.train_split_percentage, #0.90\n",
    "                      test_split_percentage=args.test_split_percentage, #0.05\n",
    "                      validation_split_percentage=args.validation_split_percentage, #0.05\n",
    "                     )\n",
    "\n",
    "    print(f\"Listing contents of {args.output_data}\")\n",
    "    dirs_output = os.listdir(args.output_data)\n",
    "    for file in dirs_output:\n",
    "        print(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process the Dataset Locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset csv (/root/.cache/huggingface/datasets/csv/data-summarization-137942e411c15b41/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Listing contents of ./data-summarization\n",
      "dialogsum-2.csv\n",
      "dialogsum-1.csv\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset loaded from path: ./data-summarization\n",
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
      "        num_rows: 14460\n",
      "    })\n",
      "})\n",
      "Loading the tokenizer for the model google/flan-t5-base\n",
      "Dataset after splitting:\n",
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
      "        num_rows: 13014\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
      "        num_rows: 723\n",
      "    })\n",
      "    validation: Dataset({\n",
      "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
      "        num_rows: 723\n",
      "    })\n",
      "})\n",
      "Tokenizing the dataset...\n"
     ]
    },
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenizing complete!\n",
      "Writing the dataset to ./data-summarization-processed\n"
     ]
    },
    {
     "data": {
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       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing complete!\n",
      "Listing contents of ./data-summarization-processed\n",
      "test\n",
      "train\n",
      "validation\n"
     ]
    }
   ],
   "source": [
    "class Args:\n",
    "    input_data: str\n",
    "    output_data: str\n",
    "    model_checkpoint: str\n",
    "    train_split_percentage: float\n",
    "    test_split_percentage: float\n",
    "    validation_split_percentage: float\n",
    "\n",
    "args = Args()\n",
    "\n",
    "args.model_checkpoint = model_checkpoint\n",
    "args.input_data = './data-summarization'\n",
    "args.output_data = './data-summarization-processed'\n",
    "args.train_split_percentage = 0.9\n",
    "args.test_split_percentage = 0.05\n",
    "args.validation_split_percentage = 0.05\n",
    "\n",
    "# remove any data that is already saved locally\n",
    "if os.path.isdir(args.output_data):\n",
    "    import shutil\n",
    "    shutil.rmtree(args.output_data)\n",
    "\n",
    "process(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensure the dataset can be loaded correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset parquet/data-summarization-processed to /root/.cache/huggingface/datasets/parquet/data-summarization-processed-38e385bef3c7b73c/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
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      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
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      "text/plain": [
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      "text/plain": [
       "Generating validation split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/parquet/data-summarization-processed-38e385bef3c7b73c/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
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      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['input_ids', 'labels'],\n",
       "        num_rows: 13014\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['input_ids', 'labels'],\n",
       "        num_rows: 723\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['input_ids', 'labels'],\n",
       "        num_rows: 723\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = load_dataset(\n",
    "    './data-summarization-processed/',\n",
    "    data_files={'train': 'train/*.parquet', 'test': 'test/*.parquet', 'validation': 'validation/*.parquet'}\n",
    ")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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