15_Evaluate_Toxicity_Fine_Tuned_Ranked_Detoxified_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REQUIRES LARGE/GPU INSTANCE TYPE\n",
    "## google/flan-t5-base toxicity score:\n",
    "Mean: 0.11131308024933918 - Std: 0.26920890184712915"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svmem(total=66814242816, available=64678019072, percent=3.2, used=1444524032, free=53346021376, active=1792905216, inactive=10226774016, buffers=2768896, cached=12020928512, shared=905216, slab=856301568)\n"
     ]
    }
   ],
   "source": [
    "import psutil\n",
    "\n",
    "notebook_memory = psutil.virtual_memory()\n",
    "print(notebook_memory)\n",
    "\n",
    "if notebook_memory.total < 32 * 1000 * 1000 * 1000:\n",
    "    print('*******************************************')    \n",
    "    print('YOU ARE NOT USING THE CORRECT INSTANCE TYPE')\n",
    "    print('PLEASE CHANGE INSTANCE TYPE TO  m5.2xlarge ')\n",
    "    print('*******************************************')\n",
    "else:\n",
    "    correct_instance_type=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r pretrained_model_checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "[ERROR] Please run the notebooks in the PREPARE section before you continue.\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    peft_lora_fine_tuned_model_checkpoint\n",
    "except NameError:\n",
    "    print(\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")\n",
    "    print(\"[ERROR] Please run the notebooks in the PREPARE section before you continue.\")\n",
    "    print(\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t5-base\n"
     ]
    }
   ],
   "source": [
    "print(pretrained_model_checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r peft_lora_fine_tuned_model_checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    peft_lora_fine_tuned_model_checkpoint\n",
    "except NameError:\n",
    "    print(\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")\n",
    "    print(\"[ERROR] Please run the notebooks in the PREPARE section before you continue.\")\n",
    "    print(\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./peft-lora-fine-tune-model-checkpoint\n"
     ]
    }
   ],
   "source": [
    "print(peft_lora_fine_tuned_model_checkpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantitative Results with ROGUE Metric\n",
    "\n",
    "The [ROUGE metric](https://en.wikipedia.org/wiki/ROUGE_(metric)) helps quantify the validity of summarizations produced by models. It compares summarizations to a \"baseline\" summary which is usually created by a human. While not perfect, it does give an indication to the overall increase in summarization effectiveness that we have accomplished by fine-tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install --disable-pip-version-check -q \\\n",
    "    transformers==4.27.2 \\\n",
    "    datasets==2.9.0 \\\n",
    "    accelerate==0.17.0 \\\n",
    "    promptsource==0.2.3 \\\n",
    "    evaluate==0.4.0 \\\n",
    "    trl==0.4.1 \\\n",
    "    rouge_score==0.1.2 \\\n",
    "    loralib==0.1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting git+https://github.com/huggingface/peft.git\n",
      "  Cloning https://github.com/huggingface/peft.git to /tmp/pip-req-build-alfhg_80\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-alfhg_80\n",
      "  Resolved https://github.com/huggingface/peft.git to commit b21559e042555a8370f4b3cf1ad581c45291cf15\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: pyyaml in /opt/conda/lib/python3.7/site-packages (from peft==0.3.0.dev0) (6.0)\n",
      "Requirement already satisfied: transformers in /opt/conda/lib/python3.7/site-packages (from peft==0.3.0.dev0) (4.27.2)\n",
      "Requirement already satisfied: psutil in /opt/conda/lib/python3.7/site-packages (from peft==0.3.0.dev0) (5.6.7)\n",
      "Requirement already satisfied: torch>=1.13.0 in /opt/conda/lib/python3.7/site-packages (from peft==0.3.0.dev0) (1.13.1)\n",
      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from peft==0.3.0.dev0) (1.21.6)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from peft==0.3.0.dev0) (23.1)\n",
      "Requirement already satisfied: accelerate in /opt/conda/lib/python3.7/site-packages (from peft==0.3.0.dev0) (0.17.0)\n",
      "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /opt/conda/lib/python3.7/site-packages (from torch>=1.13.0->peft==0.3.0.dev0) (11.10.3.66)\n",
      "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /opt/conda/lib/python3.7/site-packages (from torch>=1.13.0->peft==0.3.0.dev0) (8.5.0.96)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /opt/conda/lib/python3.7/site-packages (from torch>=1.13.0->peft==0.3.0.dev0) (11.7.99)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch>=1.13.0->peft==0.3.0.dev0) (4.5.0)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /opt/conda/lib/python3.7/site-packages (from torch>=1.13.0->peft==0.3.0.dev0) (11.7.99)\n",
      "Requirement already satisfied: wheel in /opt/conda/lib/python3.7/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.13.0->peft==0.3.0.dev0) (0.40.0)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.13.0->peft==0.3.0.dev0) (59.3.0)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from transformers->peft==0.3.0.dev0) (2.28.2)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /opt/conda/lib/python3.7/site-packages (from transformers->peft==0.3.0.dev0) (0.13.3)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /opt/conda/lib/python3.7/site-packages (from transformers->peft==0.3.0.dev0) (0.13.4)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from transformers->peft==0.3.0.dev0) (3.0.12)\n",
      "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from transformers->peft==0.3.0.dev0) (4.65.0)\n",
      "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from transformers->peft==0.3.0.dev0) (6.3.0)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers->peft==0.3.0.dev0) (2022.10.31)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->transformers->peft==0.3.0.dev0) (3.15.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers->peft==0.3.0.dev0) (2.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers->peft==0.3.0.dev0) (1.26.15)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.7/site-packages (from requests->transformers->peft==0.3.0.dev0) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers->peft==0.3.0.dev0) (2022.12.7)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install git+https://github.com/huggingface/peft.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting git+https://github.com/lvwerra/trl.git\n",
      "  Cloning https://github.com/lvwerra/trl.git to /tmp/pip-req-build-2udk1se9\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/lvwerra/trl.git /tmp/pip-req-build-2udk1se9\n",
      "  Resolved https://github.com/lvwerra/trl.git to commit 160d0c9d6cdbc8b247bad4aa8f97f2c57dc4a60f\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: torch>=1.4.0 in /opt/conda/lib/python3.7/site-packages (from trl==0.4.2.dev0) (1.13.1)\n",
      "Requirement already satisfied: transformers>=4.18.0 in /opt/conda/lib/python3.7/site-packages (from trl==0.4.2.dev0) (4.27.2)\n",
      "Requirement already satisfied: numpy>=1.18.2 in /opt/conda/lib/python3.7/site-packages (from trl==0.4.2.dev0) (1.21.6)\n",
      "Requirement already satisfied: accelerate in /opt/conda/lib/python3.7/site-packages (from trl==0.4.2.dev0) (0.17.0)\n",
      "Requirement already satisfied: datasets in /opt/conda/lib/python3.7/site-packages (from trl==0.4.2.dev0) (2.9.0)\n",
      "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /opt/conda/lib/python3.7/site-packages (from torch>=1.4.0->trl==0.4.2.dev0) (8.5.0.96)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /opt/conda/lib/python3.7/site-packages (from torch>=1.4.0->trl==0.4.2.dev0) (11.7.99)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /opt/conda/lib/python3.7/site-packages (from torch>=1.4.0->trl==0.4.2.dev0) (11.7.99)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch>=1.4.0->trl==0.4.2.dev0) (4.5.0)\n",
      "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /opt/conda/lib/python3.7/site-packages (from torch>=1.4.0->trl==0.4.2.dev0) (11.10.3.66)\n",
      "Requirement already satisfied: wheel in /opt/conda/lib/python3.7/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.4.0->trl==0.4.2.dev0) (0.40.0)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.4.0->trl==0.4.2.dev0) (59.3.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (23.1)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /opt/conda/lib/python3.7/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (0.13.3)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (2022.10.31)\n",
      "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (4.65.0)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (2.28.2)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.7/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (6.0)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /opt/conda/lib/python3.7/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (0.13.4)\n",
      "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (6.3.0)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (3.0.12)\n",
      "Requirement already satisfied: psutil in /opt/conda/lib/python3.7/site-packages (from accelerate->trl==0.4.2.dev0) (5.6.7)\n",
      "Requirement already satisfied: fsspec[http]>=2021.11.1 in /opt/conda/lib/python3.7/site-packages (from datasets->trl==0.4.2.dev0) (2023.1.0)\n",
      "Requirement already satisfied: pyarrow>=6.0.0 in /opt/conda/lib/python3.7/site-packages (from datasets->trl==0.4.2.dev0) (11.0.0)\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from datasets->trl==0.4.2.dev0) (1.3.5)\n",
      "Requirement already satisfied: multiprocess in /opt/conda/lib/python3.7/site-packages (from datasets->trl==0.4.2.dev0) (0.70.14)\n",
      "Requirement already satisfied: responses<0.19 in /opt/conda/lib/python3.7/site-packages (from datasets->trl==0.4.2.dev0) (0.18.0)\n",
      "Requirement already satisfied: aiohttp in /opt/conda/lib/python3.7/site-packages (from datasets->trl==0.4.2.dev0) (3.8.4)\n",
      "Requirement already satisfied: dill<0.3.7 in /opt/conda/lib/python3.7/site-packages (from datasets->trl==0.4.2.dev0) (0.3.6)\n",
      "Requirement already satisfied: xxhash in /opt/conda/lib/python3.7/site-packages (from datasets->trl==0.4.2.dev0) (3.2.0)\n",
      "Requirement already satisfied: asynctest==0.13.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (0.13.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (1.3.1)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (6.0.4)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (22.2.0)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (4.0.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (1.3.3)\n",
      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (2.0.4)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (1.8.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers>=4.18.0->trl==0.4.2.dev0) (1.26.15)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers>=4.18.0->trl==0.4.2.dev0) (2.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers>=4.18.0->trl==0.4.2.dev0) (2022.12.7)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->transformers>=4.18.0->trl==0.4.2.dev0) (3.15.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets->trl==0.4.2.dev0) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets->trl==0.4.2.dev0) (2019.3)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets->trl==0.4.2.dev0) (1.14.0)\n",
      "Building wheels for collected packages: trl\n",
      "  Building wheel for trl (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for trl: filename=trl-0.4.2.dev0-py3-none-any.whl size=54069 sha256=8e2af605de59092126892bf7293ccf00678f55e98d703afc8e5ae3e191557628\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-f1bwhu97/wheels/ca/6e/f4/b183ecbed483efdcd2041a8021ce7bcb9f7b09c74bff5bb00a\n",
      "Successfully built trl\n",
      "Installing collected packages: trl\n",
      "  Attempting uninstall: trl\n",
      "    Found existing installation: trl 0.4.1\n",
      "    Uninstalling trl-0.4.1:\n",
      "      Successfully uninstalled trl-0.4.1\n",
      "Successfully installed trl-0.4.2.dev0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install git+https://github.com/lvwerra/trl.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wiki_toxic (/root/.cache/huggingface/datasets/OxAISH-AL-LLM___wiki_toxic/default/1.0.0/09a67129f85f67f22107b0190f7c32050ef0dce44afeedc6e3e0ab7ab3bd709c)\n",
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/OxAISH-AL-LLM___wiki_toxic/default/1.0.0/09a67129f85f67f22107b0190f7c32050ef0dce44afeedc6e3e0ab7ab3bd709c/cache-411a66332d7a5b58.arrow\n",
      "  0%|          | 0/2 [00:00<?, ?it/s]Token indices sequence length is longer than the specified maximum sequence length for this model (780 > 512). Running this sequence through the model will result in indexing errors\n",
      " 50%|█████     | 1/2 [00:26<00:26, 26.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: google/flan-t5-base - Mean: 0.10735442655624947 - Std: 0.2648808616377917\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at ./peft_fine_tuned_with_detoxification_rewards were not used when initializing T5ForConditionalGeneration: ['v_head.summary.weight', 'v_head.summary.bias']\n",
      "- This IS expected if you are initializing T5ForConditionalGeneration from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing T5ForConditionalGeneration from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Token indices sequence length is longer than the specified maximum sequence length for this model (780 > 512). Running this sequence through the model will result in indexing errors\n",
      "100%|██████████| 2/2 [00:54<00:00, 27.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: ./peft_fine_tuned_with_detoxification_rewards - Mean: 0.10265820373335635 - Std: 0.26119035432926974\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "import csv\n",
    "\n",
    "import evaluate\n",
    "import numpy as np\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "from tqdm import tqdm\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
    "\n",
    "# https://huggingface.co/spaces/evaluate-measurement/toxicity\n",
    "toxicity_evaluator = evaluate.load(\"ybelkada/toxicity\",\n",
    "                                         \"DaNLP/da-electra-hatespeech-detection\", \n",
    "                                         module_type=\"measurement\")\n",
    "ds = load_dataset(\"OxAISH-AL-LLM/wiki_toxic\", \n",
    "                                split=\"test\")\n",
    "\n",
    "# parser = argparse.ArgumentParser(description=\"Evaluate de-toxified models\")\n",
    "# parser.add_argument(\"--model_type\", default=\"all\", type=str, help=\"Relative path to the source model folder\")\n",
    "# parser.add_argument(\"--output_file\", default=\"toxicity.csv\", type=str, help=\"Relative path to the source model folder\")\n",
    "# parser.add_argument(\"--batch_size\", default=64, type=int, help=\"Batch size\")\n",
    "# parser.add_argument(\"--num_samples\", default=400, type=int, help=\"Number of samples\")\n",
    "# parser.add_argument(\"--context_length\", default=2000, type=int, help=\"Number of samples\")\n",
    "# parser.add_argument(\"--max_new_tokens\", default=30, type=int, help=\"Max new tokens for generation\")\n",
    "# args = parser.parse_args()\n",
    "\n",
    "class Args:\n",
    "    model_type: str\n",
    "    output_file: str\n",
    "    batch_size: float\n",
    "    num_samples: float\n",
    "    content_length: float\n",
    "    max_new_tokens: str\n",
    "\n",
    "args = Args()    \n",
    "args.model_type='all'\n",
    "args.output_file='toxicity.csv'\n",
    "args.batch_size=64\n",
    "args.num_samples=400\n",
    "args.context_length=2000\n",
    "args.max_new_tokens=30\n",
    "\n",
    "if args.model_type == \"all\":\n",
    "    MODELS_TO_TEST = [\n",
    "        'google/flan-t5-base',\n",
    "#        './peft_lora_fine_tune_model_checkpoint',\n",
    "#        './peft_fine_tuned_with_public_qanda',\n",
    "        './peft_fine_tuned_with_detoxification_rewards',\n",
    "        # \"t5-base\",        \n",
    "        #supervised_fine_tuned_model_checkpoint,\n",
    "        \n",
    "    ]\n",
    "    \n",
    "pretrained_model_checkpoint='google/flan-t5-base'\n",
    "\n",
    "NUM_SAMPLES = args.num_samples\n",
    "BATCH_SIZE = args.batch_size\n",
    "output_file = args.output_file\n",
    "max_new_tokens = args.max_new_tokens\n",
    "context_length = args.context_length\n",
    "device = torch.cuda.current_device() if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "# consider only toxic prompts\n",
    "ds = ds.filter(lambda x: x[\"label\"] == 1)\n",
    "\n",
    "toxicities = {}\n",
    "\n",
    "# open a csv file\n",
    "file = open(f\"{output_file}\", \"w\", newline=\"\")\n",
    "writer = csv.writer(file)\n",
    "# add first rows\n",
    "writer.writerow([\"model_id\", \"mean_toxicity\", \"std_toxicity\"])\n",
    "\n",
    "from peft import PeftModel, PeftConfig\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
    "\n",
    "for model_id in tqdm(MODELS_TO_TEST):\n",
    "    # load base LLM model and tokenizer\n",
    "#     if 'peft' in model_id:\n",
    "#         pretrained_model = AutoModelForSeq2SeqLM.from_pretrained(pretrained_model_checkpoint, device_map={\"\": device}, torch_dtype=torch.bfloat16)  #, device_map=\"auto\")\n",
    "\n",
    "#         # Load the LoRA/PEFT model\n",
    "#         model = PeftModel.from_pretrained(pretrained_model, model_id, device_map={\"\": device}, torch_dtype=torch.bfloat16)\n",
    "#     else:\n",
    "    model = AutoModelForSeq2SeqLM.from_pretrained(model_id, device_map={\"\": device}, torch_dtype=torch.bfloat16)\n",
    "    \n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "    tokenizer.pad_token = tokenizer.eos_token\n",
    "    tokenizer.padding_side = \"left\"\n",
    "    input_texts = []\n",
    "\n",
    "    for i, example in enumerate(ds):\n",
    "        # set seed\n",
    "        torch.manual_seed(42)\n",
    "\n",
    "        input_text = example[\"comment_text\"]\n",
    "        input_texts.append(input_text[:2000])\n",
    "\n",
    "        if i > NUM_SAMPLES:\n",
    "            break\n",
    "\n",
    "        if (i + 1) % BATCH_SIZE == 0:\n",
    "            inputs = tokenizer(input_texts, return_tensors=\"pt\", padding=True).to(device)\n",
    "            inputs.input_ids = inputs.input_ids[:context_length]\n",
    "            inputs.attention_mask = inputs.attention_mask[:context_length]\n",
    "            outputs = model.generate(**inputs, do_sample=True, max_new_tokens=max_new_tokens, use_cache=True)\n",
    "            generated_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
    "            generated_texts = [\n",
    "                generated_text.replace(input_texts[i], \"\") for i, generated_text in enumerate(generated_texts)\n",
    "            ]\n",
    "            toxicity_score = toxicity_evaluator.compute(predictions=generated_texts)\n",
    "            input_texts = []\n",
    "\n",
    "            if model_id not in toxicities:\n",
    "                toxicities[model_id] = []\n",
    "            toxicities[model_id].extend(toxicity_score[\"toxicity\"])\n",
    "\n",
    "    # last batch\n",
    "    inputs = tokenizer(input_texts, return_tensors=\"pt\", padding=True).to(device)\n",
    "    outputs = model.generate(**inputs, do_sample=True, max_new_tokens=30)\n",
    "    generated_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
    "    generated_texts = [generated_text.replace(input_texts[i], \"\") for i, generated_text in enumerate(generated_texts)]\n",
    "    toxicity_score = toxicity_evaluator.compute(predictions=generated_texts)\n",
    "    toxicities[model_id].extend(toxicity_score[\"toxicity\"])\n",
    "\n",
    "    # compute mean & std using np\n",
    "    mean = np.mean(toxicities[model_id])\n",
    "    std = np.std(toxicities[model_id])\n",
    "\n",
    "    # save to file\n",
    "    writer.writerow([model_id, mean, std])\n",
    "\n",
    "    # print\n",
    "    print(f\"Model: {model_id} - Mean: {mean} - Std: {std}\")\n",
    "\n",
    "    model = None\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "# close file\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Release Resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%html\n",
    "\n",
    "# <p><b>Shutting down your kernel for this notebook to release resources.</b></p>\n",
    "# <button class=\"sm-command-button\" data-commandlinker-command=\"kernelmenu:shutdown\" style=\"display:none;\">Shutdown Kernel</button>\n",
    "        \n",
    "# <script>\n",
    "# try {\n",
    "#     els = document.getElementsByClassName(\"sm-command-button\");\n",
    "#     els[0].click();\n",
    "# }\n",
    "# catch(err) {\n",
    "#     // NoOp\n",
    "# }    \n",
    "# </script>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "availableInstances": [
   {
    "_defaultOrder": 0,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 4,
    "name": "ml.t3.medium",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 1,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.t3.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 2,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.t3.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 3,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.t3.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 4,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 5,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.m5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 6,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.m5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 7,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.m5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 8,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.m5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 9,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.m5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 10,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.m5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 11,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.m5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 12,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5d.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 13,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.m5d.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 14,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.m5d.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 15,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.m5d.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 16,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.m5d.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 17,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.m5d.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 18,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.m5d.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 19,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.m5d.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 20,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": true,
    "memoryGiB": 0,
    "name": "ml.geospatial.interactive",
    "supportedImageNames": [
     "sagemaker-geospatial-v1-0"
    ],
    "vcpuNum": 0
   },
   {
    "_defaultOrder": 21,
    "_isFastLaunch": true,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 4,
    "name": "ml.c5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 22,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.c5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 23,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.c5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 24,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.c5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 25,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 72,
    "name": "ml.c5.9xlarge",
    "vcpuNum": 36
   },
   {
    "_defaultOrder": 26,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 96,
    "name": "ml.c5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 27,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 144,
    "name": "ml.c5.18xlarge",
    "vcpuNum": 72
   },
   {
    "_defaultOrder": 28,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.c5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 29,
    "_isFastLaunch": true,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.g4dn.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 30,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.g4dn.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 31,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.g4dn.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 32,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.g4dn.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 33,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.g4dn.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 34,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.g4dn.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 35,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 61,
    "name": "ml.p3.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 36,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 244,
    "name": "ml.p3.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 37,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 488,
    "name": "ml.p3.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 38,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.p3dn.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 39,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.r5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 40,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.r5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 41,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.r5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 42,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.r5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 43,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.r5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 44,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.r5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 45,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 512,
    "name": "ml.r5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 46,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.r5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 47,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.g5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 48,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.g5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 49,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.g5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 50,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.g5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 51,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.g5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 52,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.g5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 53,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.g5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 54,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.g5.48xlarge",
    "vcpuNum": 192
   }
  ],
  "instance_type": "ml.g5.4xlarge",
  "kernelspec": {
   "display_name": "Python 3 (Data Science)",
   "language": "python",
   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/datascience-1.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}