12_Train_Ranking_Reward_Model_with_Human_Feedback_Public_QandA_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7b61ce14-5727-4ea4-a037-7cf3e34d3714",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Command\n",
    "\n",
    "torchrun --nnodes 1  --nproc_per_node 8 examples/stack_llama/scripts/reward_modeling.py --model_name=<LLAMA_SE_MODEL>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7770f11b-b09e-4e02-a24d-a79ee0cc1653",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Explore the reward dataset used to improve the model's helpfulness\n",
    "\n",
    "https://huggingface.co/datasets/lvwerra/stack-exchange-paired/viewer/lvwerra--stack-exchange-paired/train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b2cdeb5-ae1e-43df-b5ff-356ed6c7b779",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torch==1.13.1\n",
      "  Using cached torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl (887.5 MB)\n",
      "Collecting torchdata\n",
      "  Using cached torchdata-0.5.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB)\n",
      "Collecting nvidia-cuda-runtime-cu11==11.7.99\n",
      "  Using cached nvidia_cuda_runtime_cu11-11.7.99-py3-none-manylinux1_x86_64.whl (849 kB)\n",
      "Collecting nvidia-cuda-nvrtc-cu11==11.7.99\n",
      "  Using cached nvidia_cuda_nvrtc_cu11-11.7.99-2-py3-none-manylinux1_x86_64.whl (21.0 MB)\n",
      "Collecting nvidia-cudnn-cu11==8.5.0.96\n",
      "  Using cached nvidia_cudnn_cu11-8.5.0.96-2-py3-none-manylinux1_x86_64.whl (557.1 MB)\n",
      "Collecting nvidia-cublas-cu11==11.10.3.66\n",
      "  Using cached nvidia_cublas_cu11-11.10.3.66-py3-none-manylinux1_x86_64.whl (317.1 MB)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch==1.13.1) (4.5.0)\n",
      "Requirement already satisfied: wheel in /opt/conda/lib/python3.7/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch==1.13.1) (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==1.13.1) (59.3.0)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from torchdata) (2.28.2)\n",
      "Collecting portalocker>=2.0.0\n",
      "  Using cached portalocker-2.7.0-py2.py3-none-any.whl (15 kB)\n",
      "Requirement already satisfied: urllib3>=1.25 in /opt/conda/lib/python3.7/site-packages (from torchdata) (1.26.15)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->torchdata) (2.8)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.7/site-packages (from requests->torchdata) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->torchdata) (2022.12.7)\n",
      "Installing collected packages: portalocker, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cublas-cu11, nvidia-cudnn-cu11, torch, torchdata\n",
      "Successfully installed nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 portalocker-2.7.0 torch-1.13.1 torchdata-0.5.1\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 torch==1.13.1 torchdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd9e2b97-4359-462c-96ab-c61d7a252360",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "pytest-astropy 0.8.0 requires pytest-cov>=2.0, which is not installed.\n",
      "pytest-astropy 0.8.0 requires pytest-filter-subpackage>=0.1, which is not installed.\n",
      "sagemaker 2.145.0 requires importlib-metadata<5.0,>=1.4.0, but you have importlib-metadata 6.3.0 which is incompatible.\n",
      "sagemaker 2.145.0 requires PyYAML==5.4.1, but you have pyyaml 6.0 which is incompatible.\n",
      "docker-compose 1.29.2 requires PyYAML<6,>=3.10, but you have pyyaml 6.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m\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",
    "    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": 6,
   "id": "d9aa2acf-0806-4379-a7e8-a6ff2f5970f0",
   "metadata": {
    "tags": []
   },
   "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-3zsx_0hl\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-3zsx_0hl\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: 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: transformers in /opt/conda/lib/python3.7/site-packages (from peft==0.3.0.dev0) (4.27.2)\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: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from peft==0.3.0.dev0) (1.21.6)\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: pyyaml in /opt/conda/lib/python3.7/site-packages (from peft==0.3.0.dev0) (6.0)\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: 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: 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-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: 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: 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: 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: filelock in /opt/conda/lib/python3.7/site-packages (from transformers->peft==0.3.0.dev0) (3.0.12)\n",
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      "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: 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: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from transformers->peft==0.3.0.dev0) (6.3.0)\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: 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: 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: 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: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers->peft==0.3.0.dev0) (2022.12.7)\n",
      "Building wheels for collected packages: peft\n",
      "  Building wheel for peft (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for peft: filename=peft-0.3.0.dev0-py3-none-any.whl size=50384 sha256=db5eb9d67e7dd6a9a575426857b3e42cdd95c5156bf359f246d2cae672c89285\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-y4d5z9ku/wheels/13/73/6d/ff27a3703d8bad21d7e0c24cbd9dde5d7ae78f756405707a0c\n",
      "Successfully built peft\n",
      "Installing collected packages: peft\n",
      "Successfully installed peft-0.3.0.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[0m"
     ]
    }
   ],
   "source": [
    "!pip install git+https://github.com/huggingface/peft.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cdb6de36-ac03-47ff-84a5-a3982b75d266",
   "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-q2_y9stt\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/lvwerra/trl.git /tmp/pip-req-build-q2_y9stt\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-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: 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",
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      "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: 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: 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: 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: 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: 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: 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",
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      "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",
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      "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",
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      "Requirement already satisfied: multiprocess in /opt/conda/lib/python3.7/site-packages (from datasets->trl==0.4.2.dev0) (0.70.14)\n",
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      "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: 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",
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      "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",
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      "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=d8fcb7f940f67f5c382a3ced65021610281f130976657741ef6f3dcbf866382a\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-gln1nj6_/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[0m"
     ]
    }
   ],
   "source": [
    "!pip install git+https://github.com/lvwerra/trl.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f5fc934-26c5-4bce-a647-60e3b16ff7e4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "    <div class='gm-discovery-wrapper' id='gm-discovery-widget-704842b2-ef03-441d-a366-d6b7279580a2'>        <div class='gm-discovery-main-content-wrapper'>            <div class='gm-discovery-content-wrapper'>                <div class='gm-discovery-content'>                    <div class='gm-discovery-content-title'>                        Clean data in minutes                    </div>                    <div class='gm-discovery-description-import-wrapper'>                        <div class='gm-discovery-content-description'>                            <span>Automatically visualize data, and improve data quality in a few clicks.</span>                            <a class='gm-discovery-bl-link' href=https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-interactively-prepare-data-notebook.html target='_blank'>Learn more</a>                        </div>                        <button class='gm-discovery-bl-button-base gm-discovery-bl-button-variant-primary' data-commandlinker-command='sagemaker-datawrangler-discovery:import' data-commandlinker-args='{&quot;widgetId&quot;: &quot;gm-discovery-widget-704842b2-ef03-441d-a366-d6b7279580a2&quot;}'>Import Data Wrangler widget</button>                    </div>                </div>                <div class='gm-discovery-demo-img-wrapper'>                    <svg  width='487'  height='181'  viewBox='0 0 487 181'  fill='none'  xmlns='http://www.w3.org/2000/svg'  >  <rect width='487' height='181' fill='#1A1B22' />  <path d='M338.237 178.881H420.554V179.408H338.237V178.881Z' fill='#616161' />  <path d='M338.237 164.17H420.554V164.697H338.237V164.17Z' fill='#616161' />  <path d='M338.237 149.395H420.554V149.923H338.237V149.395Z' fill='#616161' />  <path d='M338.237 134.62H420.554V135.148H338.237V134.62Z' fill='#616161' />  <path d='M338.237 119.845H420.554V120.373H338.237V119.845Z' fill='#616161' />  <path d='M338.237 105.071H420.554V105.598H338.237V105.071Z' fill='#616161' />  <path d='M338.237 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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ScriptArguments(local_rank=-1, resume_from_checkpoint=False, deepspeed=None, per_device_train_batch_size=4, per_device_eval_batch_size=1, gradient_accumulation_steps=1, learning_rate=2e-05, weight_decay=0.001, model_name='roberta-base', bf16=True, num_train_epochs=1, train_subset=100000, eval_subset=50000, gradient_checkpointing=False, optim='adamw_hf', lr_scheduler_type='linear')\n"
     ]
    }
   ],
   "source": [
    "# reward_modeling.py\n",
    "\n",
    "from dataclasses import dataclass, field\n",
    "from typing import Any, Dict, List, Optional, Union\n",
    "\n",
    "import evaluate\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from datasets import load_dataset\n",
    "from peft import LoraConfig, TaskType, get_peft_model\n",
    "from transformers import (\n",
    "    AutoConfig,\n",
    "    AutoModelForSequenceClassification,\n",
    "    AutoTokenizer,\n",
    "    HfArgumentParser,\n",
    "    PreTrainedTokenizerBase,\n",
    "    Trainer,\n",
    "    TrainingArguments,\n",
    ")\n",
    "from transformers.utils import PaddingStrategy\n",
    "\n",
    "# Define and parse arguments.\n",
    "@dataclass\n",
    "class ScriptArguments:\n",
    "    \"\"\"\n",
    "    These arguments vary depending on how many GPUs you have, what their capacity and features are, and what size model you want to train.\n",
    "    \"\"\"\n",
    "\n",
    "    local_rank: Optional[int] = field(default=-1, metadata={\"help\": \"Used for multi-gpu\"})\n",
    "    resume_from_checkpoint: Optional[bool] = field(\n",
    "        default=False,\n",
    "        metadata={\"help\": \"If you want to resume training where it left off.\"},\n",
    "    )\n",
    "    deepspeed: Optional[str] = field(\n",
    "        default=None,\n",
    "        metadata={\n",
    "            \"help\": \"Path to deepspeed config if using deepspeed. You may need this if the model that you want to train doesn't fit on a single GPU.\"\n",
    "        },\n",
    "    )\n",
    "    per_device_train_batch_size: Optional[int] = field(default=4)\n",
    "    per_device_eval_batch_size: Optional[int] = field(default=1)\n",
    "    gradient_accumulation_steps: Optional[int] = field(default=1)\n",
    "    learning_rate: Optional[float] = field(default=2e-5)\n",
    "    weight_decay: Optional[int] = field(default=0.001)\n",
    "    model_name: Optional[str] = field(        \n",
    "        #default=\"gpt2\",\n",
    "        #default=\"EleutherAI/gpt-neo-125m\",\n",
    "        default=\"roberta-base\",\n",
    "        metadata={\n",
    "            \"help\": \"The model that you want to train from the Hugging Face hub. E.g. gpt2, gpt2-xl, bert, etc.\"\n",
    "        },\n",
    "    )\n",
    "    bf16: Optional[bool] = field(\n",
    "        default=True,\n",
    "        metadata={\n",
    "            \"help\": \"This essentially cuts the training time in half if you want to sacrifice a little precision and have a supported GPU.\"\n",
    "        },\n",
    "    )\n",
    "    num_train_epochs: Optional[int] = field(\n",
    "        default=1,\n",
    "        metadata={\"help\": \"The number of training epochs for the reward model.\"},\n",
    "    )\n",
    "    train_subset: Optional[int] = field(\n",
    "        default=100000,\n",
    "        metadata={\"help\": \"The size of the subset of the training data to use\"},\n",
    "    )\n",
    "    eval_subset: Optional[int] = field(\n",
    "        default=50000,\n",
    "        metadata={\"help\": \"The size of the subset of the eval data to use\"},\n",
    "    )\n",
    "    gradient_checkpointing: Optional[bool] = field(\n",
    "        default=False,\n",
    "        metadata={\"help\": \"Enables gradient checkpointing.\"},\n",
    "    )\n",
    "    optim: Optional[str] = field(\n",
    "        default=\"adamw_hf\",\n",
    "        metadata={\"help\": \"Enables gradient checkpointing.\"},\n",
    "    )\n",
    "    lr_scheduler_type: Optional[str] = field(\n",
    "        default=\"linear\",\n",
    "        metadata={\"help\": \"The lr scheduler\"},\n",
    "    )\n",
    "\n",
    "parser = HfArgumentParser(ScriptArguments)\n",
    "script_args = parser.parse_args_into_dataclasses(return_remaining_strings=True)[0]\n",
    "print(script_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28b9eb14-ac48-4e00-8eae-9641fdcaf290",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration lvwerra--stack-exchange-paired-72b1ccbd85cc93aa\n",
      "Found cached dataset parquet (/root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-72b1ccbd85cc93aa/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n",
      "Using custom data configuration lvwerra--stack-exchange-paired-b55353e62cc7a74b\n",
      "Found cached dataset parquet (/root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
     ]
    }
   ],
   "source": [
    "# Load the human stack-exchange-paired dataset for tuning the reward model.\n",
    "train_dataset = load_dataset(\"lvwerra/stack-exchange-paired\", data_dir=\"data/reward\", split=\"train\")\n",
    "if script_args.train_subset > 0:\n",
    "    train_dataset = train_dataset.select(range(script_args.train_subset))\n",
    "\n",
    "validation_dataset = load_dataset(\"lvwerra/stack-exchange-paired\", data_dir=\"data/evaluation\", split=\"train\")\n",
    "if script_args.eval_subset > 0:\n",
    "    validation_dataset = validation_dataset.select(range(script_args.eval_subset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c49bbb13-ef9c-447d-8fcc-72e82ee9b349",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.bias', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.bias']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.dense.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 1477634 || all params: 125532674 || trainable%: 1.1770911531765826\n"
     ]
    }
   ],
   "source": [
    "model_name_split = script_args.model_name.split(\"/\")[-1]\n",
    "# output_name = (\n",
    "#     f\"{model_name_split}_peft_stack-exchange-paired_rmts__{script_args.train_subset}_{script_args.learning_rate}\"\n",
    "# )\n",
    "\n",
    "\n",
    "# Load the value-head model and tokenizer.\n",
    "config = AutoConfig.from_pretrained(script_args.model_name)\n",
    "\n",
    "peft_config = LoraConfig(\n",
    "    task_type=TaskType.SEQ_CLS,\n",
    "    inference_mode=False,\n",
    "    r=8, # rank\n",
    "    lora_alpha=32,\n",
    "    lora_dropout=0.1,\n",
    ")\n",
    "\n",
    "peft_rl_ranking_reward_public_dataset_model_base = AutoModelForSequenceClassification.from_pretrained(\n",
    "    script_args.model_name, num_labels=1, torch_dtype=torch.bfloat16\n",
    ")\n",
    "peft_rl_ranking_reward_public_dataset_model = get_peft_model(peft_rl_ranking_reward_public_dataset_model_base, peft_config)\n",
    "peft_rl_ranking_reward_public_dataset_model.print_trainable_parameters()\n",
    "#peft_rl_ranking_reward_public_dataset_model.config.pad_token_id = tokenizer.eos_token_id # needed for gpt2, gpt-neo, etc\n",
    "#peft_rl_ranking_reward_public_dataset_model.config.use_cache = not script_args.gradient_checkpointing\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(script_args.model_name)\n",
    "# tokenizer.pad_token = tokenizer.eos_token # needed for gpt2, gpt-neo\n",
    "\n",
    "num_proc = 24  # Can adjust to be higher if you have more processors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c2c86af-77ee-4bd6-8353-16bd37534032",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Turn the dataset into pairs of post + summaries, where text_j is the preferred question + answer and text_k is the other.\n",
    "# Then tokenize the dataset.\n",
    "def preprocess_function(examples):\n",
    "    new_examples = {\n",
    "        \"input_ids_j\": [],\n",
    "        \"attention_mask_j\": [],\n",
    "        \"input_ids_k\": [],\n",
    "        \"attention_mask_k\": [],\n",
    "    }\n",
    "    for question, response_j, response_k in zip(examples[\"question\"], examples[\"response_j\"], examples[\"response_k\"]):\n",
    "        tokenized_j = tokenizer(\"Question: \" + question + \"\\n\\nAnswer: \" + response_j, truncation=True)\n",
    "        tokenized_k = tokenizer(\"Question: \" + question + \"\\n\\nAnswer: \" + response_k, truncation=True)\n",
    "\n",
    "        new_examples[\"input_ids_j\"].append(tokenized_j[\"input_ids\"])\n",
    "        new_examples[\"attention_mask_j\"].append(tokenized_j[\"attention_mask\"])\n",
    "        new_examples[\"input_ids_k\"].append(tokenized_k[\"input_ids\"])\n",
    "        new_examples[\"attention_mask_k\"].append(tokenized_k[\"attention_mask\"])\n",
    "\n",
    "    return new_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e480f2a3-3c9e-43c2-b1a6-cda1a646508f",
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      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-72b1ccbd85cc93aa/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-046fb9f003dba3a8.arrow\n"
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    },
    {
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    },
    {
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      " "
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    {
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     "output_type": "stream",
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      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-72b1ccbd85cc93aa/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-51876f863fc87749.arrow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-72b1ccbd85cc93aa/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-c97dceec54c1c201.arrow\n",
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     ]
    }
   ],
   "source": [
    "original_columns = train_dataset.column_names\n",
    "\n",
    "# preprocess the dataset and filter out QAs that are longer than 512\n",
    "train_dataset = train_dataset.map(preprocess_function, batched=True, num_proc=num_proc, remove_columns=original_columns)\n",
    "train_dataset = train_dataset.filter(lambda x: len(x[\"input_ids_j\"]) <= 512 and len(x[\"input_ids_k\"]) <= 512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "421434dd-a734-4081-b346-dcc1e8fbbe30",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-0363c84cc1a434d8.arrow\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-91a452c3ac1208ee.arrow\n"
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    },
    {
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     "output_type": "stream",
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      " "
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-b4aaa8c4330a4d62.arrow\n"
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    },
    {
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      " "
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    },
    {
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     "output_type": "stream",
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      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-6ffeb0fe4c869307.arrow\n"
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    },
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    },
    {
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    },
    {
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    },
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    },
    {
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      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-4b774f15c5eafdbb.arrow\n"
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     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-a0551a4045725e6b.arrow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-44d45e75de45473b.arrow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-5181e2a7888550be.arrow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-217f85848efb2350.arrow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-a15f7f84565da551.arrow\n",
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/lvwerra___parquet/lvwerra--stack-exchange-paired-b55353e62cc7a74b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-eaebc55c29ef7511.arrow\n"
     ]
    }
   ],
   "source": [
    "validation_dataset = validation_dataset.map(preprocess_function, batched=True, num_proc=num_proc, remove_columns=original_columns)\n",
    "validation_dataset = validation_dataset.filter(lambda x: len(x[\"input_ids_j\"]) <= 512 and len(x[\"input_ids_k\"]) <= 512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db988cbe-e3d6-46c7-9633-16e023645a08",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# We need to define a special data collator that batches the data in our j vs k format.\n",
    "@dataclass\n",
    "class RewardDataCollatorWithPadding:\n",
    "    tokenizer: PreTrainedTokenizerBase\n",
    "    padding: Union[bool, str, PaddingStrategy] = True\n",
    "    max_length: Optional[int] = None\n",
    "    pad_to_multiple_of: Optional[int] = None\n",
    "    return_tensors: str = \"pt\"\n",
    "\n",
    "    def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:\n",
    "        features_j = []\n",
    "        features_k = []\n",
    "        for feature in features:\n",
    "            features_j.append(\n",
    "                {\n",
    "                    \"input_ids\": feature[\"input_ids_j\"],\n",
    "                    \"attention_mask\": feature[\"attention_mask_j\"],\n",
    "                }\n",
    "            )\n",
    "            features_k.append(\n",
    "                {\n",
    "                    \"input_ids\": feature[\"input_ids_k\"],\n",
    "                    \"attention_mask\": feature[\"attention_mask_k\"],\n",
    "                }\n",
    "            )\n",
    "        batch_j = self.tokenizer.pad(\n",
    "            features_j,\n",
    "            padding=self.padding,\n",
    "            max_length=self.max_length,\n",
    "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
    "            return_tensors=self.return_tensors,\n",
    "        )\n",
    "        batch_k = self.tokenizer.pad(\n",
    "            features_k,\n",
    "            padding=self.padding,\n",
    "            max_length=self.max_length,\n",
    "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
    "            return_tensors=self.return_tensors,\n",
    "        )\n",
    "        batch = {\n",
    "            \"input_ids_j\": batch_j[\"input_ids\"],\n",
    "            \"attention_mask_j\": batch_j[\"attention_mask\"],\n",
    "            \"input_ids_k\": batch_k[\"input_ids\"],\n",
    "            \"attention_mask_k\": batch_k[\"attention_mask\"],\n",
    "            \"return_loss\": True,\n",
    "        }\n",
    "        return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "19e84b9b-707f-4af2-ac7b-8d0e4f9348b0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the metric that we'll use for validation.\n",
    "accuracy = evaluate.load(\"accuracy\")\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    predictions, _ = eval_pred\n",
    "    # Here, predictions is rewards_j and rewards_k.\n",
    "    # We want to see how much of the time rewards_j > rewards_k.\n",
    "    predictions = np.argmax(predictions, axis=0)\n",
    "    labels = np.zeros(predictions.shape)\n",
    "    return accuracy.compute(predictions=predictions, references=labels)\n",
    "\n",
    "\n",
    "class RewardTrainer(Trainer):\n",
    "    # Define how to compute the reward loss. We use the InstructGPT pairwise logloss: https://arxiv.org/abs/2203.02155\n",
    "    def compute_loss(self, model, inputs, return_outputs=False):\n",
    "        rewards_j = model(input_ids=inputs[\"input_ids_j\"], attention_mask=inputs[\"attention_mask_j\"])[0]\n",
    "        rewards_k = model(input_ids=inputs[\"input_ids_k\"], attention_mask=inputs[\"attention_mask_k\"])[0]\n",
    "        loss = -nn.functional.logsigmoid(rewards_j - rewards_k).mean()\n",
    "        if return_outputs:\n",
    "            return loss, {\"rewards_j\": rewards_j, \"rewards_k\": rewards_k}\n",
    "        return loss\n",
    "\n",
    "\n",
    "peft_rl_ranking_reward_public_dataset_model_checkpoint_name='./peft_rl_ranking_reward_public_dataset_model/'\n",
    "    \n",
    "# Define the training args. Needs to be done before the model is loaded if you are using deepspeed.\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=peft_rl_ranking_reward_public_dataset_model_checkpoint_name,\n",
    "    learning_rate=script_args.learning_rate,\n",
    "    per_device_train_batch_size=script_args.per_device_train_batch_size,\n",
    "    per_device_eval_batch_size=script_args.per_device_eval_batch_size,\n",
    "    num_train_epochs=script_args.num_train_epochs,\n",
    "    weight_decay=script_args.weight_decay,\n",
    "#    evaluation_strategy=\"steps\",\n",
    "#   eval_steps=500,\n",
    "#   save_strategy=\"steps\",\n",
    "#   save_steps=500,\n",
    "    gradient_accumulation_steps=script_args.gradient_accumulation_steps,\n",
    "    gradient_checkpointing=script_args.gradient_checkpointing,\n",
    "    deepspeed=script_args.deepspeed,\n",
    "    local_rank=script_args.local_rank,\n",
    "    remove_unused_columns=False,\n",
    "    label_names=[],\n",
    "    bf16=script_args.bf16,\n",
    "    logging_strategy=\"steps\",\n",
    "    logging_steps=1000,\n",
    "    #max_steps=100,\n",
    "    optim=script_args.optim,\n",
    "    lr_scheduler_type=script_args.lr_scheduler_type,\n",
    ")\n",
    "\n",
    "# Train the reward model, finally!\n",
    "reward_trainer = RewardTrainer(\n",
    "    model=peft_rl_ranking_reward_public_dataset_model,\n",
    "    args=training_args,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=validation_dataset,\n",
    "    compute_metrics=compute_metrics,\n",
    "    data_collator=RewardDataCollatorWithPadding(tokenizer=tokenizer, max_length=512),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29930aae-afa1-4d68-be93-deeff0fa0556",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/transformers/optimization.py:395: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n",
      "You're using a RobertaTokenizerFast 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",
      "/opt/conda/lib/python3.7/site-packages/transformers/tokenization_utils_base.py:2365: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
      "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
      "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='2196' max='25000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [ 2196/25000 05:42 < 59:16, 6.41 it/s, Epoch 0.09/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.694700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>0.691600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/transformers/tokenization_utils_base.py:2365: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
      "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
      "/opt/conda/lib/python3.7/site-packages/transformers/tokenization_utils_base.py:2365: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
      "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
      "/opt/conda/lib/python3.7/site-packages/transformers/tokenization_utils_base.py:2365: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
      "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
      "/opt/conda/lib/python3.7/site-packages/transformers/tokenization_utils_base.py:2365: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
      "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n"
     ]
    }
   ],
   "source": [
    "reward_trainer.train(script_args.resume_from_checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bedef8f-208c-4193-93ec-35987cfd2b76",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(\"Saving last checkpoint of the model to {}\".format(peft_rl_ranking_reward_public_dataset_model_checkpoint_name))\n",
    "peft_rl_ranking_reward_public_dataset_model.save_pretrained(peft_rl_ranking_reward_public_dataset_model_checkpoint_name)\n",
    "\n",
    "reward_trainer.model.save_pretrained(peft_rl_ranking_reward_public_dataset_model_checkpoint_name)\n",
    "\n",
    "tokenizer.save_pretrained(peft_rl_ranking_reward_public_dataset_model_checkpoint_name)  #    output_name + \"rl_reward_model\")??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef704c71-b0e2-43c5-96b2-d624036c8ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%store peft_rl_ranking_reward_public_dataset_model_checkpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdf86c6a-f8ec-4757-9e34-05bc0bcaefae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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