polyglot_qlora_vicuna.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jwj7140/polyglot_qlora_vicuna/blob/main/polyglot_qlora_vicuna.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIyP_0r6zuVc"
      },
      "source": [
        "# `transformers` meets `bitsandbytes` for democratzing Large Language Models (LLMs) through 4bit quantization\n",
        "\n",
        "<center>\n",
        "<img src=\"https://github.com/huggingface/blog/blob/main/assets/96_hf_bitsandbytes_integration/Thumbnail_blue.png?raw=true\" alt=\"drawing\" width=\"700\" class=\"center\"/>\n",
        "</center>\n",
        "\n",
        "Welcome to this notebook that goes through the recent `bitsandbytes` integration that includes the work from XXX that introduces no performance degradation 4bit quantization techniques, for democratizing LLMs inference and training.\n",
        "\n",
        "In this notebook, we will learn together how to load a large model in 4bit (`gpt-neo-x-20b`) and train it using Google Colab and PEFT library from Hugging Face 🤗.\n",
        "\n",
        "[In the general usage notebook](https://colab.research.google.com/drive/1ge2F1QSK8Q7h0hn3YKuBCOAS0bK8E0wf?usp=sharing), you can learn how to propely load a model in 4bit with all its variants.\n",
        "\n",
        "If you liked the previous work for integrating [*LLM.int8*](https://arxiv.org/abs/2208.07339), you can have a look at the [introduction blogpost](https://huggingface.co/blog/hf-bitsandbytes-integration) to lean more about that quantization method.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHX-RCPdkXgP",
        "outputId": "875e6c11-2191-42ef-c533-e92c9cb9fcbd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Jun 18 02:01:23 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   46C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FuXIFTFapAMI",
        "outputId": "d5469d58-8138-4dce-a515-71d53b799a8e",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/92.2 MB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJ-5idQwzvg-"
      },
      "source": [
        "First let's load the model we are going to use - GPT-neo-x-20B! Note that the model itself is around 40GB in half precision"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        "id": "jm4FzCvfeYcK",
        "outputId": "c8336f99-0738-491b-f321-f714596a921a"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/3.78k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/changpt--ko-lima-vicuna to /root/.cache/huggingface/datasets/changpt___json/changpt--ko-lima-vicuna-2a780ec698677b36/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bec99f47eff4416f880786152cfd7c5e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.94M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cbc1a97de6294d92a47a6b76d9658d25"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fae724936d994d9da59efc07e5812cb4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "101e5f9238024c40b3f83f5c13e81159"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/changpt___json/changpt--ko-lima-vicuna-2a780ec698677b36/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9b8a1bd529b452cb1146365a6030f53"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "data = load_dataset(\"changpt/ko-lima-vicuna\", data_files=\"ko_lima_vicuna.json\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KUhV7x3e6Db",
        "outputId": "ad2d8a1b-2a33-4402-bf7d-2e4b61c90567"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['conversations', 'id'],\n",
              "        num_rows: 866\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
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        "id": "0FbgsI9sezTJ",
        "outputId": "aaa206de-5be0-4313-e2a5-a30f4b6a41b9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/866 [00:00<?, ? examples/s]"
            ],
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          "metadata": {}
        }
      ],
      "source": [
        "data = data.map(\n",
        "    lambda x: {\n",
        "        'text': \"\\n\".join([f\"{'인간' if line['from']=='human' else '비서'}: {line['value']}{'</끝>' if line['from']!='human' else ''}\" for line in x['conversations']])\n",
        "      }\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//172.28.0.1'), PosixPath('8013'), PosixPath('http')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-21vk4c8z1kgbw --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//ipykernel.pylab.backend_inline'), PosixPath('module')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/28 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3ea6033aec454adca4ccf178be0b4272"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cef2fd06c190428880713a2fffe2afc9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "model_id = \"EleutherAI/polyglot-ko-12.8b\"\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mp2gMi1ZzGET"
      },
      "source": [
        "Then we have to apply some preprocessing to the model to prepare it for training. For that use the `prepare_model_for_kbit_training` method from PEFT."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55,
          "referenced_widgets": [
            "e78c2b60b1554109a47463bcdfa459f5",
            "df878482475c4af38fa9dc590d02ff46",
            "2b19c7c306d149de8b4f06b4feef066c",
            "149206b9f05e45e2b31c86302a2c74ee",
            "f45ef2e78efe4eb9b72b78359fabbc85",
            "b15c898a0ff647d383344d994003827e",
            "c7c02088185d49258801ec3a6dd073e5",
            "fba75d52fcf94cf9b3f49dd43d89ca33",
            "c030e6d948a240dca1125c92d93b6539",
            "f6798d0da6dc420db8168d7770a69cfb",
            "1ce3d062e89f4963b93da074209b0f33"
          ]
        },
        "id": "C4TDUgDbhyhK",
        "outputId": "3fb86efe-5c73-4997-ec4e-594c61cc735f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/866 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e78c2b60b1554109a47463bcdfa459f5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"
          ]
        }
      ],
      "source": [
        "data = data.map(lambda samples: tokenizer(samples[\"text\"],max_length=2048), batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "2LkOWpsSet1n",
        "outputId": "4d405b06-af40-4bc0-9414-71c27455457e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'인간: 20문제 게임을 해볼까요? 제가 생각하는 동물이 있어요.\\n비서: 20문제 게임 좋아요!\\n\\n1. 포유류인가요?</끝>\\n인간: 아니요\\n비서: 2. 물 속에서 살고 있나요?</끝>\\n인간: 아니요\\n비서: 3. 파충류인가요?</끝>\\n인간: 네\\n비서: 4. 다리가 4개인가요?</끝>\\n인간: 네\\n비서: 5. 포식자인가요?</끝>\\n인간: 네\\n비서: 6. 길이가 1미터(3피트)보다 큰가요?</끝>\\n인간: 네\\n비서: 7. 아프리카에 서식하는가요?</끝>\\n인간: 아니요\\n비서: 8. 아시아에 서식하는가요?</끝>\\n인간: 네\\n비서: 9. 코모도 드래곤인가요?</끝>\\n인간: 맞아요!\\n비서: 만세! 9번의 질문으로 당신이 생각한 동물을 맞췄어요: 코모도 드래곤.\\n\\n재미있는 사실: 코모도 드래곤은 현재 가장 큰 도마뱀입니다!</끝>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "data['train'][260]['text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "a9EUEDAl0ss3"
      },
      "outputs": [],
      "source": [
        "from peft import prepare_model_for_kbit_training\n",
        "\n",
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "gkIcwsSU01EB"
      },
      "outputs": [],
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ybeyl20n3dYH",
        "outputId": "deb4a401-f238-4add-c429-019796ce512b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 6553600 || all params: 6608701440 || trainable%: 0.09916622894073424\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=8,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"query_key_value\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FCc64bfnmd3j"
      },
      "source": [
        "Let's load a common dataset, english quotes, to fine tune our model on famous quotes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "BXrLpt5ket1o"
      },
      "outputs": [],
      "source": [
        "tokenNum_ai = 12266     # \"비서\"\n",
        "tokenNum_human = 8301   # \"인간\"\n",
        "tokenNum_com = 29        # \":\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6f4z8EYmcJ6",
        "outputId": "63a78338-b094-4228-fcb9-b305213f53f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Jun 18 02:09:42 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   55C    P0    27W /  70W |   9375MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "xZgRpDU8et1p"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "from transformers import Trainer\n",
        "import numpy as np\n",
        "\n",
        "class maskTrainer(Trainer):\n",
        "  def __init__(self, *args, **kwargs):\n",
        "    super().__init__(*args, **kwargs)\n",
        "\n",
        "  def compute_loss(self, model, inputs, return_outputs=False):\n",
        "    # maskIndex_ai = torch.eq(inputs['input_ids'], torch.tensor(tokenNum_ai)).nonzero()\n",
        "    # maskIndex_human = torch.eq(inputs['input_ids'], torch.tensor(tokenNum_human)).nonzero()\n",
        "    # print(maskIndex_ai)\n",
        "\n",
        "    for x in range(len(inputs['labels'])):\n",
        "      # maskIndex_ai = torch.eq(inputs['input_ids'], torch.tensor(tokenNum_ai)).nonzero()\n",
        "      # maskIndex_human = torch.eq(inputs['input_ids'], torch.tensor(tokenNum_human)).nonzero()\n",
        "      # print(maskIndex_ai)\n",
        "\n",
        "      maskindex1 = (inputs['labels'][x]==tokenNum_human).nonzero()[:, 0]\n",
        "      temp = 0\n",
        "      for i, index in enumerate(maskindex1):\n",
        "        if (inputs['labels'][x][index+1] != tokenNum_com):\n",
        "          maskindex1 = np.delete(maskindex1, i-temp)\n",
        "          temp += 1\n",
        "\n",
        "      maskindex2 = (inputs['labels'][x]==tokenNum_ai).nonzero()[:, 0]\n",
        "      temp = 0\n",
        "      for i, index in enumerate(maskindex2):\n",
        "        if (inputs['labels'][x][index+1] != tokenNum_com):\n",
        "          maskindex2 = np.delete(maskindex2, i-temp)\n",
        "          temp += 1\n",
        "\n",
        "      for i in range(len(maskindex1)):\n",
        "        ai_index = -1\n",
        "        for num in maskindex2:\n",
        "          if (maskindex1[i] < num):\n",
        "            ai_index = num\n",
        "            break\n",
        "        if (ai_index == -1):\n",
        "          inputs['labels'][x][maskindex1[i]+2:] = -100\n",
        "        else:\n",
        "          inputs['labels'][x][maskindex1[i]+2:ai_index+2] = -100\n",
        "\n",
        "    # print(inputs['labels'][x])\n",
        "\n",
        "    outputs = model(**inputs)\n",
        "\n",
        "    loss = outputs['loss']\n",
        "\n",
        "    return (loss,outputs) if return_outputs else loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0MOtwf3zdZp"
      },
      "source": [
        "Run the cell below to run the training! For the sake of the demo, we just ran it for few steps just to showcase how to use this integration with existing tools on the HF ecosystem."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jq0nX33BmfaC",
        "outputId": "0342b506-3136-4cbe-edda-3a893a4e8eda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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",
            "  warnings.warn(\n",
            "You're using a PreTrainedTokenizerFast 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='400' max='400' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [400/400 36:32, Epoch 0/1]\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>100</td>\n",
              "      <td>2.230800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>2.025300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.902900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.990100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=400, training_loss=2.0372779083251955, metrics={'train_runtime': 2199.5737, 'train_samples_per_second': 0.182, 'train_steps_per_second': 0.182, 'total_flos': 8374420468285440.0, 'train_loss': 2.0372779083251955, 'epoch': 0.46})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# import transformers\n",
        "\n",
        "# # needed for gpt-neo-x tokenizer\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "trainer = maskTrainer(\n",
        "    model=model,\n",
        "    train_dataset=data[\"train\"],\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=1,\n",
        "        # warmup_steps=200,\n",
        "        max_steps=400, ## 초소형만 학습: 10 step = 20개 샘플만 학습.\n",
        "        fp16=True,\n",
        "        output_dir=\"outputs\",\n",
        "        logging_steps=100,\n",
        "        # num_train_epochs = 4,\n",
        "        learning_rate=5e-4,\n",
        "\n",
        "        lr_scheduler_type= \"cosine\",\n",
        "        #optim=\"paged_adamw_8bit\"\n",
        "\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "TvP11yUbhzWP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "875bd612-9736-4b02-9c5c-66f03b40525f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "wow\n"
          ]
        }
      ],
      "source": [
        "print(\"wow\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "a-jauOEv9XVe"
      },
      "outputs": [],
      "source": [
        "model.eval()\n",
        "model.config.use_cache = True  # silence the warnings. Please re-enable for inference!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "_j-kDDF_et1p"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"./saved\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import StoppingCriteria, StoppingCriteriaList\n",
        "\n",
        "class StoppingCriteriaSub(StoppingCriteria):\n",
        "\n",
        "    def __init__(self, stops = [], encounters=1):\n",
        "        super().__init__()\n",
        "        self.stops = [stop for stop in stops]\n",
        "\n",
        "    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):\n",
        "        for stop in self.stops:\n",
        "            if torch.all((stop == input_ids[0][-len(stop):])).item():\n",
        "                return True\n",
        "\n",
        "        return False\n",
        "\n",
        "stop_words = [\"</끝>\"]\n",
        "stop_words_ids = [tokenizer(stop_word, return_tensors='pt')['input_ids'].squeeze() for stop_word in stop_words]\n",
        "stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])"
      ],
      "metadata": {
        "id": "LiiRjl8-YsBt"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "oDp9W-Gmp5Mb"
      },
      "outputs": [],
      "source": [
        "def gen(x):\n",
        "    prompt = f\"인간: {x}\\n비서:\"\n",
        "    gened = model.generate(\n",
        "        **tokenizer(\n",
        "            prompt,\n",
        "            return_tensors='pt',\n",
        "            return_token_type_ids=False\n",
        "        ),\n",
        "        max_new_tokens=512,\n",
        "        temperature=0.8,\n",
        "        # no_repeat_ngram_size=3,\n",
        "        early_stopping=True,\n",
        "        do_sample=True,\n",
        "        eos_token_id=2,\n",
        "        stopping_criteria=stopping_criteria\n",
        "    )\n",
        "    return tokenizer.decode(gened[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
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              "'인간: 슈카월드가 무엇인가요?\\n비서: 슈카월드는 쉽게 말하면 유튜버입니다. 콘텐츠로는 게임, 경제, 기술 등 다양한 주제를 다루고 있으며, 특히 게임 해설을 많이 합니다.\\n\\n슈카는 미국의 유명한 유튜버로, 매주 1억 명의 방문객을 유치하고 있습니다(세계 2위).\\n�이 유튜버는 일반적으로 1인칭 게임을 주로 다루는 것으로 유명하지만, 현재는 다양한 게임을 다루고 있으며, 가끔은 게임이 아닌 주제도 다루고 있습니다.\\n\\n슈카는 게임 해설의 인기와 자신의 재능을 기반으로 전업 유튜버로 일하고 있습니다.\\n\\n유명함에 따라, 그는 경제를 다루는 다른 유튜버들과 달리, 슈카 월드에서 구독자들에게 돈을 버는 방법에 대해서는 거의 언급하지 않습니다. 이런 것들은 슈카가 돈을 벌기 위해 하는 일이 아닌 유튜버로 일하면서 하는 일이기 때문입니다.\\n�슈카는 주로 게임과 게임을 즐기는 사람들에 초점을 맞추고 있기 때문에, 그가 하는 대부분의 말은 게임과 관련된 것입니다. 예를 들어 그는 \"Graphics are better(그래픽이 더 좋다)\"와 같은 단순한 말을 하곤 합니다. 이는 그가 게임에 더 몰입할 수 있도록 해줍니다.\\n\\n이러한 것을 보고 슈카의 말이 재미없다고 생각하실 수도 있고, 그의 말을 진지하게 받아들이는 것에 대해 거북함을 느끼실 수도 있습니다. 하지만 그럼에도 그의 인기는 슈카 월드의 인기가 게임 외의 다른 주제를 다루는 유튜버들보다 게임을 즐기는 사람들에게 더 많은 관심을 받고 있다는 것을 보여줍니다. 또한 그가 하는 게임과 관련된 말들은 꽤 재미있습니다. 그는 게임뿐만 아니라 다른 게임을 즐기는 사람들에게도 인기가 있을 것입니다.</끝>'"
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