{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-Tuning GPT-2 with RLHF on Drugs.com Reviews for High-Quality Drug Reviews on Depression\n",
        "\n",
        "\n",
        "**Author**: Zakia Salod\n",
        "\n",
        "**Affiliation**: University of KwaZulu-Natal (UKZN), Durban, South Africa\n",
        "\n",
        "**Contact**: zakia.salod@gmail.com\n",
        "\n",
        "**Machine Used**: Google Colab T4 GPU\n",
        "\n",
        "**Last Updated**: 10 December 2023\n",
        "\n",
        "**Description**:\n",
        "This notebook demonstrates fine-tuning the GPT-2 model (specifically, Zakia/gpt2-drugscom_depression_reviews) using Reinforcement Learning with Human Feedback (RLHF), leveraging the TRL (transformer reinforcement learning) library. The base model (GPT-2) and reward model (DistilBERT, specifically, Zakia/distilbert-drugscom_depression_reviews) are both fine-tuned on the same Drugs.com reviews dataset, focusing on depression. The goal is to further refine the GPT-2 model's ability to generate high-quality patient reviews on depression drugs, using RLHF for targeted improvement. This approach aims to harness the strengths of both GPT-2 and DistilBERT in generating insightful and accurate text content.\n",
        "\n",
        "\n",
        "**License**:\n",
        "This work is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). Free for educational and research use.\n",
        "\n"
      ],
      "metadata": {
        "id": "McyCLRHCRADe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<div style=\"text-align: center\">\n",
        "  <img src='https://huggingface.co/Zakia/gpt2-drugscom_depression_reviews-hq-v1/resolve/main/images/RLHF_DepressionReviews_Flow.png' width='800'>\n",
        "  <p style=\"text-align: center;\">\n",
        "    <b>Figure 1:</b> This diagram represents the RLHF process applied to the GPT-2 model (<a href=\"https://huggingface.co/Zakia/gpt2-drugscom_depression_reviews-hq-v1\">link</a>) using the DrugsCom DepressionReviews dataset. The fine-tuned GPT-2 model (<a href=\"https://huggingface.co/Zakia/gpt2-drugscom_depression_reviews\">link</a>) shown in purple, DistilBERT model (<a href=\"https://huggingface.co/Zakia/distilbert-drugscom_depression_reviews\">link</a>) depicted in orange, and the dataset (<a href=\"https://huggingface.co/datasets/Zakia/drugscom_reviews\">link</a>, filtered for 'Depression' condition in the 'train' set) mentioned in the turquoise box, are highlighted to show their integration in the fine-tuning process.</p></div>"
      ],
      "metadata": {
        "id": "flWxHE44V_gh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 1: SETTING UP THE ENVIRONMENT"
      ],
      "metadata": {
        "id": "2LWsWZo5K0c1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load Necessary Libraries"
      ],
      "metadata": {
        "id": "ta54fa0GLBzP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Enable automatic module reloading to reflect changes in external .py files\n",
        "%load_ext autoreload\n",
        "# Reload all modules before executing code, keeping modules up-to-date\n",
        "%autoreload 2"
      ],
      "metadata": {
        "id": "OQoTv4uQhxGa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install Required Packages"
      ],
      "metadata": {
        "id": "ZWPOCVIsLLpt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LNu3PqsV4NNo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46070413-cf44-4583-896c-3ad8985430c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: bottle, waitress, smmap, shtab, setproctitle, sentry-sdk, pyarrow-hotfix, docstring-parser, docker-pycreds, dill, multiprocess, gitdb, Accelerator, tyro, GitPython, accelerate, wandb, datasets, trl, peft\n",
            "Successfully installed Accelerator-2023.11.3.dev1 GitPython-3.1.40 accelerate-0.25.0 bottle-0.12.25 datasets-2.15.0 dill-0.3.7 docker-pycreds-0.4.0 docstring-parser-0.15 gitdb-4.0.11 multiprocess-0.70.15 peft-0.7.0 pyarrow-hotfix-0.6 sentry-sdk-1.38.0 setproctitle-1.3.3 shtab-1.6.5 smmap-5.0.1 trl-0.7.4 tyro-0.6.0 waitress-2.1.2 wandb-0.16.1\n"
          ]
        }
      ],
      "source": [
        "!pip install Accelerator peft trl wandb"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import Necessary Libraries"
      ],
      "metadata": {
        "id": "PSvI3SMCLSgK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HdOCuPm91iKd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d8433b9-ef28-4719-b293-209fa870be8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from dataclasses import dataclass, field\n",
        "from typing import Optional\n",
        "import pandas as pd\n",
        "import re\n",
        "import html\n",
        "import numpy as np\n",
        "import random\n",
        "\n",
        "import torch\n",
        "from accelerate import Accelerator\n",
        "from datasets import load_dataset\n",
        "from peft import LoraConfig\n",
        "from tqdm import tqdm\n",
        "from transformers import AutoTokenizer, pipeline\n",
        "from datasets import concatenate_datasets\n",
        "\n",
        "from trl import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead, PPOConfig, PPOTrainer, set_seed\n",
        "from trl.core import LengthSampler\n",
        "from trl.import_utils import is_xpu_available"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZE2hblIQ11lI"
      },
      "outputs": [],
      "source": [
        "tqdm.pandas()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set Random Seeds for Reproducibility"
      ],
      "metadata": {
        "id": "iWSnSLePLf2e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed_value = 42\n",
        "\n",
        "random.seed(seed_value)\n",
        "torch.manual_seed(seed_value)"
      ],
      "metadata": {
        "id": "WLhPyBaWiBll",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3788113-1664-425e-bf01-9aa14c35a885"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7dfebd1905b0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Initialize Weights & Biases for Tracking"
      ],
      "metadata": {
        "id": "_TpL4t1yLrWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import wandb\n",
        "\n",
        "wandb.init(project=\"gpt2-drugscom_depression_reviews-hq-v1\")"
      ],
      "metadata": {
        "id": "83T68a9XiHHs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "a84d1912-1487-4303-d75c-165e11ddeb35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.1"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20231210_103320-e2k30lm5</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/team-zakia/gpt2-drugscom_depression_reviews-hq-v1/runs/e2k30lm5' target=\"_blank\">devoted-pyramid-6</a></strong> to <a href='https://wandb.ai/team-zakia/gpt2-drugscom_depression_reviews-hq-v1' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/team-zakia/gpt2-drugscom_depression_reviews-hq-v1' target=\"_blank\">https://wandb.ai/team-zakia/gpt2-drugscom_depression_reviews-hq-v1</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/team-zakia/gpt2-drugscom_depression_reviews-hq-v1/runs/e2k30lm5' target=\"_blank\">https://wandb.ai/team-zakia/gpt2-drugscom_depression_reviews-hq-v1/runs/e2k30lm5</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/team-zakia/gpt2-drugscom_depression_reviews-hq-v1/runs/e2k30lm5?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
            ],
            "text/plain": [
              "<wandb.sdk.wandb_run.Run at 0x7dfde84f8610>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 2: CONFIGURATION"
      ],
      "metadata": {
        "id": "JGTvUvvrL_mD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define script arguments for training configuration"
      ],
      "metadata": {
        "id": "W329hvPKQZEy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lT6DOtQE13gO"
      },
      "outputs": [],
      "source": [
        "@dataclass\n",
        "class ScriptArguments:\n",
        "    ppo_config: PPOConfig = field(\n",
        "        default_factory=lambda: PPOConfig(\n",
        "            model_name=\"Zakia/gpt2-drugscom_depression_reviews\",\n",
        "            query_dataset=\"Zakia/drugscom_reviews\",\n",
        "            reward_model=\"sentiment-analysis:Zakia/distilbert-drugscom_depression_reviews\",\n",
        "            learning_rate=1.41e-5,\n",
        "            log_with=\"wandb\",\n",
        "            mini_batch_size=128,\n",
        "            batch_size=128,\n",
        "            gradient_accumulation_steps=1,\n",
        "            early_stopping=False,\n",
        "            target_kl=6.0,\n",
        "            kl_penalty=\"kl\",\n",
        "            seed=0,\n",
        "            use_score_scaling=False,\n",
        "            use_score_norm=False,\n",
        "            score_clip=None,\n",
        "        )\n",
        "    )\n",
        "    use_seq2seq: bool = False\n",
        "    \"\"\"whether to use seq2seq models\"\"\"\n",
        "    use_peft: bool = False\n",
        "    \"\"\"whether to use peft\"\"\"\n",
        "    peft_config: Optional[LoraConfig] = field(\n",
        "        default_factory=lambda: LoraConfig(\n",
        "            r=16,\n",
        "            lora_alpha=16,\n",
        "            bias=\"none\",\n",
        "            task_type=\"CAUSAL_LM\",\n",
        "        ),\n",
        "    )\n",
        "    trust_remote_code: bool = field(default=False, metadata={\"help\": \"Enable `trust_remote_code`\"})"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Initialize script arguments"
      ],
      "metadata": {
        "id": "r2VBoJb3QdSv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7NVo3ViFCaXq"
      },
      "outputs": [],
      "source": [
        "args = ScriptArguments(\n",
        "    ppo_config=PPOConfig(\n",
        "        model_name=\"Zakia/gpt2-drugscom_depression_reviews\",\n",
        "        query_dataset=\"Zakia/drugscom_reviews\",\n",
        "        reward_model=\"sentiment-analysis:Zakia/distilbert-drugscom_depression_reviews\",\n",
        "        learning_rate=1.41e-5,\n",
        "        log_with=\"wandb\",\n",
        "        mini_batch_size=128,\n",
        "        batch_size=128,\n",
        "        gradient_accumulation_steps=1,\n",
        "        early_stopping=False,\n",
        "        target_kl=6.0,\n",
        "        kl_penalty=\"kl\",\n",
        "        seed=0,\n",
        "        use_score_scaling=False,\n",
        "        use_score_norm=False,\n",
        "        score_clip=None,\n",
        "    ),\n",
        "    use_seq2seq=False,\n",
        "    use_peft=False,\n",
        "    peft_config=LoraConfig(\n",
        "        r=16,\n",
        "        lora_alpha=16,\n",
        "        bias=\"none\",\n",
        "        task_type=\"CAUSAL_LM\",\n",
        "    ),\n",
        "    trust_remote_code=False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xXRNMIgu2G8u"
      },
      "outputs": [],
      "source": [
        "# We then define the arguments to pass to the sentiment analysis pipeline.\n",
        "# We set `return_all_scores` to True to get the sentiment score for each token.\n",
        "sent_kwargs = {\"return_all_scores\": True, \"function_to_apply\": \"none\", \"batch_size\": 16}\n",
        "\n",
        "# Select appropriate model class based on arguments\n",
        "trl_model_class = AutoModelForCausalLMWithValueHead if not args.use_seq2seq else AutoModelForSeq2SeqLMWithValueHead"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 3: DATASET PREPARATION"
      ],
      "metadata": {
        "id": "OW8FrsVUMbtu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XRyup-PP2dp-"
      },
      "outputs": [],
      "source": [
        "# Function to clean review text\n",
        "def clean_review(text):\n",
        "    # Check if the text is a string\n",
        "    if not isinstance(text, str):\n",
        "      return \"\"  # Return an empty string if the input is not a string\n",
        "    text = html.unescape(text)  # Decode HTML entities\n",
        "    text = re.sub(r'\"', '', text)  # Remove quotes\n",
        "    text = re.sub(r'<.*?>', '', text)  # Remove HTML tags\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mbAzTGjW2eJZ"
      },
      "outputs": [],
      "source": [
        "# Clean the reviews of the dataset\n",
        "# Apply the clean_review function in a batched manner\n",
        "def clean_reviews(batch):\n",
        "    # Apply clean_review to each review in the batch and return the modified batch\n",
        "    return {\"review\": [clean_review(review) for review in batch[\"review\"]]}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dZx9ibFT2J0F"
      },
      "outputs": [],
      "source": [
        "# Function to build and preprocess the dataset\n",
        "def build_dataset(config, query_dataset, input_min_text_length=2, input_max_text_length=8):\n",
        "    \"\"\"\n",
        "    Build dataset for training. This builds the dataset from `load_dataset`\n",
        "\n",
        "    Args:\n",
        "        query_dataset (`str`):\n",
        "            The name of the dataset to be loaded.\n",
        "\n",
        "    Returns:\n",
        "        dataloader (`torch.utils.data.DataLoader`):\n",
        "            The dataloader for the dataset.\n",
        "    \"\"\"\n",
        "    tokenizer = AutoTokenizer.from_pretrained(config.model_name)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "    # Load the dataset\n",
        "    ds = load_dataset(query_dataset, split=\"train\")\n",
        "\n",
        "    # Filter the dataset for the condition 'Depression'\n",
        "    ds = ds.filter(lambda x: x[\"condition\"] == \"Depression\")\n",
        "\n",
        "    # Filter out (remove) rows with missing drugName, or review\n",
        "    ds = ds.filter(lambda x: all([x.get(\"drugName\"), x.get(\"review\")]))\n",
        "\n",
        "    # Clean the reviews\n",
        "    ds = ds.map(clean_reviews, batched=True)\n",
        "\n",
        "    # Get the number of records\n",
        "    num_records = ds.num_rows\n",
        "    print(f\"Number of records with Depression condition: {num_records}\")\n",
        "\n",
        "    input_size = LengthSampler(input_min_text_length, input_max_text_length)\n",
        "\n",
        "    # Tokenization\n",
        "    def tokenize(sample):\n",
        "        sample[\"input_ids\"] = tokenizer.encode(sample[\"review\"])[: input_size()]\n",
        "        sample[\"query\"] = tokenizer.decode(sample[\"input_ids\"])\n",
        "        return sample\n",
        "\n",
        "    ds = ds.map(tokenize, batched=False)\n",
        "\n",
        "    ds.set_format(type=\"torch\")\n",
        "    return ds"
      ]
    },
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              "model_id": "b7d4d69778694809a93365fd2624cd81"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of records with Depression condition: 9069\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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      ],
      "source": [
        "# Load and preprocess the dataset\n",
        "# We retrieve the dataloader by calling the `build_dataset` function.\n",
        "dataset = build_dataset(args.ppo_config, args.ppo_config.query_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 4: MODEL INITIALIZATION"
      ],
      "metadata": {
        "id": "-WIoZ2n8NMdk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a-qMNamD2PSz"
      },
      "outputs": [],
      "source": [
        "def collator(data):\n",
        "    return dict((key, [d[key] for d in data]) for key in data[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LH-3QcMo2RQ5"
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      "outputs": [],
      "source": [
        "# Set seed before initializing value head for deterministic eval\n",
        "set_seed(args.ppo_config.seed)"
      ]
    },
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        "outputId": "47670793-100f-4af5-849f-4c10ba0e1898"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:A <class 'transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel'> model is loaded from 'Zakia/gpt2-drugscom_depression_reviews', and no v_head weight is found. This IS expected if you are not resuming PPO training.\n"
          ]
        }
      ],
      "source": [
        "# Now let's build the model, the reference model, and the tokenizer.\n",
        "if not args.use_peft:\n",
        "    ref_model = trl_model_class.from_pretrained(args.ppo_config.model_name, trust_remote_code=args.trust_remote_code)\n",
        "    device_map = None\n",
        "    peft_config = None\n",
        "else:\n",
        "    peft_config = args.peft_config\n",
        "    ref_model = None\n",
        "    # Copy the model to each device\n",
        "    device_map = {\"\": Accelerator().local_process_index}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B9rCL7qq2TGY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8b6cec5-7154-4343-9854-bfdac871b8d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:A <class 'transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel'> model is loaded from 'Zakia/gpt2-drugscom_depression_reviews', and no v_head weight is found. This IS expected if you are not resuming PPO training.\n"
          ]
        }
      ],
      "source": [
        "model = trl_model_class.from_pretrained(\n",
        "    args.ppo_config.model_name,\n",
        "    trust_remote_code=args.trust_remote_code,\n",
        "    device_map=device_map,\n",
        "    peft_config=peft_config,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lHWI6nnr2XaQ"
      },
      "outputs": [],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(args.ppo_config.model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xx9_1KgQ2ZI5"
      },
      "outputs": [],
      "source": [
        "# Some tokenizers like GPT-2's don't have a padding token by default, so we set one here.\n",
        "tokenizer.pad_token_id = tokenizer.eos_token_id"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 5: INITIALIZE PPO TRAINER"
      ],
      "metadata": {
        "id": "bMeivsaCNp9Q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "outputId": "eaadc134-f9af-41b8-edae-fc2c4c2f0ce7"
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      "outputs": [
        {
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            "text/plain": [
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              " View run <strong style=\"color:#cdcd00\">devoted-pyramid-6</strong> at: <a href='https://wandb.ai/team-zakia/gpt2-drugscom_depression_reviews-hq-v1/runs/e2k30lm5' target=\"_blank\">https://wandb.ai/team-zakia/gpt2-drugscom_depression_reviews-hq-v1/runs/e2k30lm5</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "Find logs at: <code>./wandb/run-20231210_103320-e2k30lm5/logs</code>"
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              "Successfully finished last run (ID:e2k30lm5). Initializing new run:<br/>"
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              "<IPython.core.display.HTML object>"
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            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/team-zakia/trl/runs/ku689w1y' target=\"_blank\">feasible-meadow-29</a></strong> to <a href='https://wandb.ai/team-zakia/trl' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/team-zakia/trl' target=\"_blank\">https://wandb.ai/team-zakia/trl</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/team-zakia/trl/runs/ku689w1y' target=\"_blank\">https://wandb.ai/team-zakia/trl/runs/ku689w1y</a>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# We then build the PPOTrainer, passing the model, the reference model, the tokenizer\n",
        "ppo_trainer = PPOTrainer(args.ppo_config, model, ref_model, tokenizer, dataset=dataset, data_collator=collator)"
      ]
    },
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      "cell_type": "code",
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        {
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            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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      ],
      "source": [
        "# We then build the sentiment analysis pipeline, passing the model name and the\n",
        "# sentiment analysis pipeline arguments. Let's also make sure to set the device\n",
        "# to the same device as the PPOTrainer.\n",
        "device = ppo_trainer.accelerator.device\n",
        "if ppo_trainer.accelerator.num_processes == 1:\n",
        "    if is_xpu_available():\n",
        "        device = \"xpu:0\"\n",
        "    else:\n",
        "        device = 0 if torch.cuda.is_available() else \"cpu\"  # to avoid a `pipeline` bug\n",
        "ds_plugin = ppo_trainer.accelerator.state.deepspeed_plugin\n",
        "task, model_name = args.ppo_config.reward_model.split(\":\")\n",
        "if ds_plugin is not None and ds_plugin.is_zero3_init_enabled():\n",
        "    with ds_plugin.zero3_init_context_manager(enable=False):\n",
        "        sentiment_pipe = pipeline(task, model=model_name, device=device)\n",
        "else:\n",
        "    sentiment_pipe = pipeline(task, model=model_name, device=device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aN7GYpvW2g30"
      },
      "outputs": [],
      "source": [
        "# Some tokenizers like GPT-2's don't have a padding token by default, so we set one here.\n",
        "if sentiment_pipe.tokenizer.pad_token_id is None:\n",
        "    sentiment_pipe.tokenizer.pad_token_id = tokenizer.pad_token_id\n",
        "\n",
        "if sentiment_pipe.model.config.pad_token_id is None:\n",
        "    sentiment_pipe.model.config.pad_token_id = tokenizer.pad_token_id"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"This medication has changed my life for the better. I've experienced no side effects and my symptoms of depression have significantly decreased.\"\n",
        "sentiment_pipe(text, **sent_kwargs)"
      ],
      "metadata": {
        "id": "yeujUJUwjO_T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b22ec64-5c06-4240-bbed-5e7f4f566923"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_classification.py:105: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'label': 'LOW_QUALITY_REVIEW', 'score': -2.689751625061035},\n",
              "  {'label': 'HIGH_QUALITY_REVIEW', 'score': 2.4064526557922363}]]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"I've had a terrible experience with this medication. It made me feel nauseous and I didn't notice any improvement in my condition.\"\n",
        "sentiment_pipe(text, **sent_kwargs)"
      ],
      "metadata": {
        "id": "RyW7YWPSjQbH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a86be4c3-bb19-4088-dfcc-5d9777986101"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'label': 'LOW_QUALITY_REVIEW', 'score': 3.4417612552642822},\n",
              "  {'label': 'HIGH_QUALITY_REVIEW', 'score': -3.960636615753174}]]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generation Settings\n",
        "For the response generation we just use sampling and make sure top-k and nucleus sampling are turned off as well as a minimal length."
      ],
      "metadata": {
        "id": "k89v_UY6OXhQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kT6NPyFl2iqo"
      },
      "outputs": [],
      "source": [
        "# We then define the arguments to pass to the `generate` function. These arguments\n",
        "# are passed to the `generate` function of the PPOTrainer, which is a wrapper around\n",
        "# the `generate` function of the trained model.\n",
        "generation_kwargs = {\n",
        "    \"min_length\": -1,\n",
        "    \"top_k\": 0.0,\n",
        "    \"top_p\": 1.0,\n",
        "    \"do_sample\": True,\n",
        "    \"pad_token_id\": tokenizer.eos_token_id,\n",
        "    \"max_new_tokens\": 32,\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 6: TRAINING LOOP FOR FINE-TUNING WITH REINFORCEMENT LEARNING WITH HUMAN FEEDBACK (RLHF)"
      ],
      "metadata": {
        "id": "HqA9AW6qO2Vn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ugaSRCkU2mKb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f53d139-d3c7-408a-acaf-302c1bd0c8be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "0it [00:00, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "4it [02:48, 41.74s/it]/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1101: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "70it [46:05, 39.51s/it]\n"
          ]
        }
      ],
      "source": [
        "# Training loop for RLHF\n",
        "for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):\n",
        "    query_tensors = batch[\"input_ids\"]\n",
        "\n",
        "    # Get response from gpt2\n",
        "    response_tensors, ref_response_tensors = ppo_trainer.generate(\n",
        "        query_tensors, return_prompt=False, generate_ref_response=True, **generation_kwargs\n",
        "    )\n",
        "    batch[\"response\"] = tokenizer.batch_decode(response_tensors)\n",
        "    batch[\"ref_response\"] = tokenizer.batch_decode(ref_response_tensors)\n",
        "\n",
        "    # Compute sentiment score\n",
        "    texts = [q + r for q, r in zip(batch[\"query\"], batch[\"response\"])]\n",
        "    pipe_outputs = sentiment_pipe(texts, **sent_kwargs)\n",
        "    rewards = [torch.tensor(output[1][\"score\"]) for output in pipe_outputs]\n",
        "    ref_texts = [q + r for q, r in zip(batch[\"query\"], batch[\"ref_response\"])]\n",
        "    ref_pipe_outputs = sentiment_pipe(ref_texts, **sent_kwargs)\n",
        "    ref_rewards = [torch.tensor(output[1][\"score\"]) for output in ref_pipe_outputs]\n",
        "    batch[\"ref_rewards\"] = ref_rewards\n",
        "\n",
        "    # Run PPO step\n",
        "    stats = ppo_trainer.step(query_tensors, response_tensors, rewards)\n",
        "    ppo_trainer.log_stats(stats, batch, rewards, columns_to_log=[\"query\", \"response\", \"ref_response\", \"ref_rewards\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TRAINING PROGRESS WITH WEIGHTS & BIASES"
      ],
      "metadata": {
        "id": "e7OWmw2Pdva8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This section of the notebook is dedicated to visualizing and interpreting the training progress using the Weights & Biases platform. By integrating with Weights & Biases, we gain access to valuable visual insights that help us understand how the model's performance evolves over time. Refer to the interactive report on wandb.ai at: [GPT-2 RLHF DepressionReviews Report](https://wandb.ai/team-zakia/trl/reports/GPT-2-RLHF-DepressionReviews-Report--Vmlldzo2MjMxNjk5?accessToken=2bjrzj5pa1rspudwgw38hgv0rqamtpfgd30feha7vkzht01hkrytet9vy82x2rvk). An overview of the Reward Mean Plot and the Reward Distribution Heatmap Plot from this report can be found below, with the plots in Figures 2 and 3, respectively."
      ],
      "metadata": {
        "id": "-sC7YH3peUwe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reward Mean Plot"
      ],
      "metadata": {
        "id": "u9Jpi_72em4q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<div style=\"text-align: center\">\n",
        "<img src='https://huggingface.co/Zakia/gpt2-drugscom_depression_reviews-hq-v1/resolve/main/images/reward_mean_plot.png' width='800'>\n",
        "<p style=\"text-align: center;\"> <b>Figure 2:</b> env/reward_mean plot showing the average reward per training step. </p>\n",
        "</div>"
      ],
      "metadata": {
        "id": "OXSYzDTEfQ_c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This plot represents the average reward that the model received at each step of the training process. A reward in this context is a numerical value that indicates how well the generated text aligns with high-quality standards as determined by the reward model. The upward trend in the plot suggests that the model consistently starts to generate more positive and aligned outputs as training progresses, indicating that the RLHF technique is effectively improving the model's performance."
      ],
      "metadata": {
        "id": "FL3MN1NNftq0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reward Distribution Heatmap Plot"
      ],
      "metadata": {
        "id": "bjAICf9XerQS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<div style=\"text-align: center\">\n",
        "<img src='https://huggingface.co/Zakia/gpt2-drugscom_depression_reviews-hq-v1/resolve/main/images/reward_distribution_heatmap.png' width='800'>\n",
        "<p style=\"text-align: center;\"> <b>Figure 3:</b> env/reward_dist heatmap plot showing the distribution of rewards over training steps. </p>\n",
        "</div>"
      ],
      "metadata": {
        "id": "SqAlkuGegBR_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This heatmap illustrates the distribution of rewards over training steps. Each vertical slice of the plot can be thought of as a snapshot of the reward landscape at a given step, with the color intensity representing the frequency of rewards at different levels. As the training continues, we expect to see the color bands shift upwards, which would indicate that the model is more frequently generating higher-quality responses."
      ],
      "metadata": {
        "id": "2RqTT-dRgZro"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 7: EVALUATE THE MODEL"
      ],
      "metadata": {
        "id": "1jJoW0FZPERB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VLjobu0AoWTN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "outputId": "35c5b8c3-3669-4e26-9c55-4338516bce63"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_classification.py:105: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1101: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          query  \\\n",
              "0         Very Very good. Helps   \n",
              "1           It worked for about   \n",
              "2                 Started on 20   \n",
              "3                     I am a 43   \n",
              "4                  I got on Pro   \n",
              "5      This drug has changed me   \n",
              "6    Good Med! Little skeptical   \n",
              "7             I used to take cl   \n",
              "8                Been on Prozac   \n",
              "9                  I've been on   \n",
              "10   I've been taking this pill   \n",
              "11             Was put on 30mg.   \n",
              "12            I was addicted to   \n",
              "13   I've been on approximately   \n",
              "14   Super good for depression.   \n",
              "15       Hyped me up like crazy   \n",
              "16                    I am a 32   \n",
              "17  My only complaint is that I   \n",
              "18                 I've been on   \n",
              "19             Lexapro has been   \n",
              "\n",
              "                                    response (before)  \\\n",
              "0   Very Very good. Helps to deal with some of the...   \n",
              "1    a month. The nausea is gone and I no longer f...   \n",
              "2    mg.  It seems to help somewhat with some of t...   \n",
              "3    year old woman with severe anxiety and depres...   \n",
              "4   zac.  I would sleep all night, feel sick one d...   \n",
              "5    drastically.  A year after taking an XL I bec...   \n",
              "6    at first, because some things that seem so go...   \n",
              "7     onazepam for 4 years and that caused much worse   \n",
              "8    for 11 years now and still getting worse as t...   \n",
              "9    Pristiq for 1 week now. The first 1 day was h...   \n",
              "10     for 6 months and I feel 100% better after just   \n",
              "11   Still depressed the first 3 months. Now I'm o...   \n",
              "12   opiates for years when I was younger and I co...   \n",
              "13   10 mg for six months and it's taking away som...   \n",
              "14  Super good for depression.  Diarrhea is better...   \n",
              "15  , as did my MGs who said they thought this was...   \n",
              "16  Y male and have been on up to 300 mg of zoloft...   \n",
              "17   complaint is that I experience extreme foggy ...   \n",
              "18   this 300mg for just a little over a year and ...   \n",
              "19   better than anything I've tried to battle my ...   \n",
              "\n",
              "                                     response (after)  rewards (before)  \\\n",
              "0    me with extreme depression and anxiety. Can h...         -1.911440   \n",
              "1    6 months and I feel so much better that I've ...          1.584692   \n",
              "2   mg for 4 days now...I feel great. I've been si...         -3.230841   \n",
              "3    year old mother of two and a mother of two da...          0.613143   \n",
              "4   I got on Prozac for about two years. Prozac re...         -3.422938   \n",
              "5    from an excited procrastinator! I feel like I...         -3.109789   \n",
              "6     Good Med! Little skeptical & SEXy!<|endoftext|>         -3.691891   \n",
              "7                 onazepam and 5mg prozac at the same         -1.095920   \n",
              "8    for 13 years. I have never felt this good in ...         -1.745274   \n",
              "9   I've been on this for over three months, I fee...         -0.377317   \n",
              "10         for almost 14 years and I took I F 3 times          1.914182   \n",
              "11   It made me feel much better! I could finally ...         -3.148672   \n",
              "12   SSRI's, hallucinogens, and drug- just therapy...         -3.175338   \n",
              "13   two dozen different SSRI's over the past thir...         -3.352825   \n",
              "14  Super good for depression. I am 5'1 and I have...          1.200642   \n",
              "15   before and I'm a great mom now! I work as an ...         -4.075667   \n",
              "16  yr old male and live in a very nice suburb (wi...          0.437383   \n",
              "17   feel fatigued during the day. And also depres...         -4.083458   \n",
              "18  I've been on this anti-depressant for over 18 ...         -0.072659   \n",
              "19  Lexapro has been AMAZING, so far. I've been on...         -1.699961   \n",
              "\n",
              "    rewards (after)  \n",
              "0          2.428029  \n",
              "1          2.306569  \n",
              "2          2.124902  \n",
              "3          2.237940  \n",
              "4          2.157211  \n",
              "5          0.991710  \n",
              "6         -1.087502  \n",
              "7          1.416539  \n",
              "8          2.083820  \n",
              "9          2.041740  \n",
              "10         2.104877  \n",
              "11         2.363128  \n",
              "12         0.608868  \n",
              "13         2.426083  \n",
              "14         1.900504  \n",
              "15         1.796120  \n",
              "16         2.238822  \n",
              "17        -3.843148  \n",
              "18         0.724089  \n",
              "19         1.292072  "
            ],
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              "      <th></th>\n",
              "      <th>query</th>\n",
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              "      <th>0</th>\n",
              "      <td>Very Very good. Helps</td>\n",
              "      <td>Very Very good. Helps to deal with some of the...</td>\n",
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              "      <th>1</th>\n",
              "      <td>It worked for about</td>\n",
              "      <td>a month. The nausea is gone and I no longer f...</td>\n",
              "      <td>6 months and I feel so much better that I've ...</td>\n",
              "      <td>1.584692</td>\n",
              "      <td>2.306569</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Started on 20</td>\n",
              "      <td>mg.  It seems to help somewhat with some of t...</td>\n",
              "      <td>mg for 4 days now...I feel great. I've been si...</td>\n",
              "      <td>-3.230841</td>\n",
              "      <td>2.124902</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I am a 43</td>\n",
              "      <td>year old woman with severe anxiety and depres...</td>\n",
              "      <td>year old mother of two and a mother of two da...</td>\n",
              "      <td>0.613143</td>\n",
              "      <td>2.237940</td>\n",
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              "      <td>I got on Pro</td>\n",
              "      <td>zac.  I would sleep all night, feel sick one d...</td>\n",
              "      <td>I got on Prozac for about two years. Prozac re...</td>\n",
              "      <td>-3.422938</td>\n",
              "      <td>2.157211</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>This drug has changed me</td>\n",
              "      <td>drastically.  A year after taking an XL I bec...</td>\n",
              "      <td>from an excited procrastinator! I feel like I...</td>\n",
              "      <td>-3.109789</td>\n",
              "      <td>0.991710</td>\n",
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              "      <th>6</th>\n",
              "      <td>Good Med! Little skeptical</td>\n",
              "      <td>at first, because some things that seem so go...</td>\n",
              "      <td>Good Med! Little skeptical &amp; SEXy!&lt;|endoftext|&gt;</td>\n",
              "      <td>-3.691891</td>\n",
              "      <td>-1.087502</td>\n",
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              "      <th>7</th>\n",
              "      <td>I used to take cl</td>\n",
              "      <td>onazepam for 4 years and that caused much worse</td>\n",
              "      <td>onazepam and 5mg prozac at the same</td>\n",
              "      <td>-1.095920</td>\n",
              "      <td>1.416539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Been on Prozac</td>\n",
              "      <td>for 11 years now and still getting worse as t...</td>\n",
              "      <td>for 13 years. I have never felt this good in ...</td>\n",
              "      <td>-1.745274</td>\n",
              "      <td>2.083820</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>I've been on</td>\n",
              "      <td>Pristiq for 1 week now. The first 1 day was h...</td>\n",
              "      <td>I've been on this for over three months, I fee...</td>\n",
              "      <td>-0.377317</td>\n",
              "      <td>2.041740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>I've been taking this pill</td>\n",
              "      <td>for 6 months and I feel 100% better after just</td>\n",
              "      <td>for almost 14 years and I took I F 3 times</td>\n",
              "      <td>1.914182</td>\n",
              "      <td>2.104877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Was put on 30mg.</td>\n",
              "      <td>Still depressed the first 3 months. Now I'm o...</td>\n",
              "      <td>It made me feel much better! I could finally ...</td>\n",
              "      <td>-3.148672</td>\n",
              "      <td>2.363128</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>I was addicted to</td>\n",
              "      <td>opiates for years when I was younger and I co...</td>\n",
              "      <td>SSRI's, hallucinogens, and drug- just therapy...</td>\n",
              "      <td>-3.175338</td>\n",
              "      <td>0.608868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>I've been on approximately</td>\n",
              "      <td>10 mg for six months and it's taking away som...</td>\n",
              "      <td>two dozen different SSRI's over the past thir...</td>\n",
              "      <td>-3.352825</td>\n",
              "      <td>2.426083</td>\n",
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              "      <th>14</th>\n",
              "      <td>Super good for depression.</td>\n",
              "      <td>Super good for depression.  Diarrhea is better...</td>\n",
              "      <td>Super good for depression. I am 5'1 and I have...</td>\n",
              "      <td>1.200642</td>\n",
              "      <td>1.900504</td>\n",
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              "      <th>15</th>\n",
              "      <td>Hyped me up like crazy</td>\n",
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              "      <td>-4.075667</td>\n",
              "      <td>1.796120</td>\n",
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              "      <th>16</th>\n",
              "      <td>I am a 32</td>\n",
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              "      <td>0.437383</td>\n",
              "      <td>2.238822</td>\n",
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              "      <td>My only complaint is that I</td>\n",
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              "      <td>-4.083458</td>\n",
              "      <td>-3.843148</td>\n",
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              "      <td>I've been on this anti-depressant for over 18 ...</td>\n",
              "      <td>-0.072659</td>\n",
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              "      <td>-1.699961</td>\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_506b96b2-abea-4371-aa2b-79f67f675612 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "output_min_length = 10\n",
        "output_max_length = 50\n",
        "output_length_sampler = LengthSampler(output_min_length, output_max_length)\n",
        "\n",
        "#### Get a batch from the dataset\n",
        "bs = 20\n",
        "game_data = dict()\n",
        "dataset.set_format(\"pandas\")\n",
        "df_batch = dataset[:].sample(bs)\n",
        "game_data[\"query\"] = df_batch[\"query\"].tolist()\n",
        "query_tensors = df_batch[\"input_ids\"].tolist()\n",
        "\n",
        "response_tensors_ref, response_tensors = [], []\n",
        "\n",
        "#### Get response from GPT2 and GPT2_REF\n",
        "for i in range(bs):\n",
        "    gen_len = output_length_sampler()\n",
        "\n",
        "    # Update generation_kwargs with the dynamic max_new_tokens value\n",
        "    dynamic_generation_kwargs = generation_kwargs.copy()\n",
        "    dynamic_generation_kwargs['max_new_tokens'] = gen_len\n",
        "\n",
        "    output = ref_model.generate(\n",
        "        torch.tensor(query_tensors[i]).unsqueeze(dim=0).to(device), **dynamic_generation_kwargs\n",
        "    ).squeeze()[-gen_len:]\n",
        "    response_tensors_ref.append(output)\n",
        "\n",
        "    output = model.generate(\n",
        "        torch.tensor(query_tensors[i]).unsqueeze(dim=0).to(device), **dynamic_generation_kwargs\n",
        "    ).squeeze()[-gen_len:]\n",
        "    response_tensors.append(output)\n",
        "\n",
        "#### Decode responses\n",
        "game_data[\"response (before)\"] = [tokenizer.decode(response_tensors_ref[i]) for i in range(bs)]\n",
        "game_data[\"response (after)\"] = [tokenizer.decode(response_tensors[i]) for i in range(bs)]\n",
        "\n",
        "#### Sentiment analysis of query/response pairs before/after\n",
        "texts = [q + r for q, r in zip(game_data[\"query\"], game_data[\"response (before)\"])]\n",
        "game_data[\"rewards (before)\"] = [output[1][\"score\"] for output in sentiment_pipe(texts, **sent_kwargs)]\n",
        "\n",
        "texts = [q + r for q, r in zip(game_data[\"query\"], game_data[\"response (after)\"])]\n",
        "game_data[\"rewards (after)\"] = [output[1][\"score\"] for output in sentiment_pipe(texts, **sent_kwargs)]\n",
        "\n",
        "# Store results in a dataframe\n",
        "df_results = pd.DataFrame(game_data)\n",
        "df_results"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at the reward mean/median of the generated sequences we observe a significant difference."
      ],
      "metadata": {
        "id": "UCZqr9bhPgQ8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0xiQcEO_tcT8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "da58cc66-31a6-46e7-bddb-5a2ac4b0ca7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "rewards (before)   -1.622197\n",
              "rewards (after)     1.415619\n",
              "dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "median:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "rewards (before)   -1.828357\n",
              "rewards (after)     2.062780\n",
              "dtype: float64"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\"mean:\")\n",
        "display(df_results[[\"rewards (before)\", \"rewards (after)\"]].mean())\n",
        "print()\n",
        "print(\"median:\")\n",
        "display(df_results[[\"rewards (before)\", \"rewards (after)\"]].median())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 8: SAVE THE FINE-TUNED GPT-2 MODEL: gpt2-drugscom_depression_reviews-hq-v1"
      ],
      "metadata": {
        "id": "77DlUMK_PlWv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1aKcZ0sWtlQq"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sEQVHh8ztnlC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484,
          "referenced_widgets": [
            "06614bb33a674d7c9529dba7237e29f2",
            "effe58d07862430c9f14fb6a8f0d9339",
            "a71cb398750c4c5691b30243f7a80c80",
            "cbaa0c4d6c6849f98e4b631a9611227b",
            "bff1b054f9b64676816ebb84827d9d77",
            "755ee2ee027848c79dfbb836b87e4968",
            "26bb5803b0ce442d91cc36a96d412372",
            "d53284c8dcd14b9390aad1c8f2baa51b",
            "e3879a4fe4914aa3a3eb81977b8d85bf",
            "41d0df2831114687937cd8c601006b77",
            "0e6fcb61bf02408eb6bdfaa00bc35f4c",
            "ec3e0454cb814b1991711056b51bf3f1",
            "62b05b7dec9e40229836f0c7125963e4",
            "a3d025fee186451a83fb8c69d59efca2",
            "350521650a3d46ce91c80a74036e6522",
            "4ab9212ce7e945dfa714e212c3203c79",
            "1478c65fa2db433e85479e80ca0edf1a",
            "c4c301ccd6184a8393eaf9b7200b8fc0",
            "7b49a91fabcc4ccab7a53bf90d2ff3ee",
            "ef5ca4e85fd84d66b416440fc4e93a1a",
            "ee37eb8f5a69425c929dde0d945b8e1c",
            "cbbeb89e584246a2b79ea9bb3896b766",
            "97fbd74292ea4250aae33e90fc9c5f12",
            "b0afc5b4436348d1a12265337cebeb6d",
            "d73d45eafd6e45a8b9bcd9f69863820f",
            "5293daa1efc64a18a1d421ce3c922932",
            "d81a469ab0e341818e0ba77539c6e9ae",
            "0360660e0ca243318cc19d79aca7d469",
            "585b9c1d27b947c682228091f6dbf13f",
            "f4d1be3572a04a6bb8d29e0026ec13b5",
            "1b643386556b44e4a244407165336202",
            "2ede2fedf6474bea9ae143c670fd10fc"
          ]
        },
        "outputId": "73dfc078-35c1-4caa-ae45-d0e41cda6885"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "06614bb33a674d7c9529dba7237e29f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "NotImplementedError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-1b9c03e266bc>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Login to Hugging Face within the notebook\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mnotebook_login\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'git config --global credential.helper store'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_shell.py\u001b[0m in \u001b[0;36msystem\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     97\u001b[0m       \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'also_return_output'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_system_commands\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_system_compat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint:disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpip_warn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_system_compat\u001b[0;34m(shell, cmd, also_return_output)\u001b[0m\n\u001b[1;32m    452\u001b[0m   \u001b[0;31m# is expected to call this function, thus adding one level of nesting to the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m   \u001b[0;31m# stack.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m   result = _run_command(\n\u001b[0m\u001b[1;32m    455\u001b[0m       \u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclear_streamed_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m   )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_run_command\u001b[0;34m(cmd, clear_streamed_output)\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0mlocale_encoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlocale\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetpreferredencoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlocale_encoding\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0m_ENCODING\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m       raise NotImplementedError(\n\u001b[0m\u001b[1;32m    169\u001b[0m           \u001b[0;34m'A UTF-8 locale is required. Got {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocale_encoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m       )\n",
            "\u001b[0;31mNotImplementedError\u001b[0m: A UTF-8 locale is required. Got ANSI_X3.4-1968"
          ]
        }
      ],
      "source": [
        "# Login to Hugging Face within the notebook\n",
        "notebook_login()\n",
        "!git config --global credential.helper store"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4BMQTQlJtqpj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
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        "outputId": "44dfdabf-a968-4a15-ba94-013669b0ca97"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/498M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('gpt2-drugscom_depression_reviews-hq-v1/tokenizer_config.json',\n",
              " 'gpt2-drugscom_depression_reviews-hq-v1/special_tokens_map.json',\n",
              " 'gpt2-drugscom_depression_reviews-hq-v1/vocab.json',\n",
              " 'gpt2-drugscom_depression_reviews-hq-v1/merges.txt',\n",
              " 'gpt2-drugscom_depression_reviews-hq-v1/added_tokens.json',\n",
              " 'gpt2-drugscom_depression_reviews-hq-v1/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# Save and push your model to the hub\n",
        "model.save_pretrained(\"gpt2-drugscom_depression_reviews-hq-v1\", push_to_hub=True)\n",
        "tokenizer.save_pretrained(\"gpt2-drugscom_depression_reviews-hq-v1\", push_to_hub=True)"
      ]
    }
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