GenAI_hw5_LLM_finetuning.ipynb

{
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/HuizhiXu/GENERATIVE-AI-2024-SPRING/blob/main/GenAI_hw5_LLM_finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uh5rwbr4q5Nw"
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      "source": [
        "## GenAI HW5: LLM Fine-tuning\n",
        "In this homework, you will fine-tune your own LLM to enable the LLM to write Tang poems. For more details, please refer to [homework slides](https://docs.google.com/presentation/d/1NINc4Av4pJDMsGL1N_B4CUviwU13-H0RiCvs2K01O-I/edit#slide=id.g265f1963f2a_0_5)\n",
        "\n",
        "**TODOs**\n",
        "1. Read the slides and make sure you know the objectives of this homework.\n",
        "2. Save a copy of this Colab notebook.\n",
        "3. Follow the steps in this Colab notebook to fine-tune your LLM.\n",
        "4. Evaluate outputs using DaVinci Assistant (達哥批改助教)\n",
        "5. Update results to NTU COOL\n",
        "6. (optional) use [score parsing program](https://colab.research.google.com/drive/1OIuGKjmKghIgHtBlsWcGLcFP0pNQRNQE?usp=sharing) to check your score\n",
        "\n",
        "If you have any questions, please contact the TAs via TA hours, NTU COOL, or email to ntu-gen-ai-2024-spring-ta@googlegroups.com\n",
        "\n",
        "**Tips**: At the top of each cell, it shows whether you need to change hyperparameters in that cell and how long it might take."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eRKf5DA69b3r"
      },
      "source": [
        "## Activate GPU\n",
        "Since you will fine-tune a model, you **MUST** activate GPU so that this homework can be done in reasonable time (1~2 hrs)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **MUST READ**:\n",
        "\n",
        "Colab does **NOT** guarantee the GPU access for free user ([ref](https://research.google.com/colaboratory/faq.html#idle-timeouts)). It is possible you get an message saying \"Cannot connect to GPU backend\" which means there are no enough GPU resources for you now. When this happens, you may need to **wait for one (or more) day or login different Google account to do the homework**."
      ],
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      "source": [
        "### Enable GPU\n",
        "\n",
        "1. Click on \"Runtime\" (or \"執行階段\") in the header.\n",
        "2. Click on \"Change runtime type\" (or \"變更執行階段類型\") in the drop menu.\n",
        "3. Select \"T4 GPU\" and save. (You can select \"A100 GPU\" or \"V100 GPU\" if you have Colab Pro)\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RrXWZefE-WXJ"
      },
      "source": [
        "## Mount Google Drive\n",
        "You **MUST** mount your Google Drive so that the  results can be saved on it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SFKTMRc2-fs1"
      },
      "source": [
        "To mount Google Drive, please follow these steps:\n",
        "\n",
        "1. Run the next cell.\n",
        "2. Click on \"Connect to Google Drive\". (There should be a window popup)\n",
        "3. Sign in your account and give permission."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J8cXGoOcCLrK"
      },
      "source": [
        "The following code block takes about **1** minutes to run, but it may vary depending on the condition of Colab and Google Drive."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4irqfznNAlrZ",
        "outputId": "433e63ad-764c-4091-c102-fc7aea6da34c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hJFrdFQn84M"
      },
      "source": [
        "## Install Packages\n",
        "We install and import some well-written packages created by others to facilitate the fine-tuning process."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4UoAVpcAELzB"
      },
      "source": [
        "The following code block takes about **5** minutes to run, but it may vary depending on the condition of Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kuRMjk0rtWBx",
        "outputId": "b51344b8-d4ca-4986-b340-a6abb8cfbf0f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting bitsandbytes==0.43.0\n",
            "  Downloading bitsandbytes-0.43.0-py3-none-manylinux_2_24_x86_64.whl (102.2 MB)\n",
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            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from bitsandbytes==0.43.0) (2.2.1+cu121)\n",
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            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
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            "\u001b[?25hCollecting nvidia-cuda-cupti-cu12==12.1.105 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
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            "\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
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            "\u001b[?25hCollecting nvidia-cublas-cu12==12.1.3.1 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
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            "\u001b[?25hCollecting nvidia-cufft-cu12==11.0.2.54 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
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            "\u001b[?25hCollecting nvidia-curand-cu12==10.3.2.106 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
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            "\u001b[?25hCollecting nvidia-cusolver-cu12==11.4.5.107 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
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            "\u001b[?25hCollecting nvidia-cusparse-cu12==12.1.0.106 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
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            "\u001b[?25hCollecting nvidia-nccl-cu12==2.19.3 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
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            "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
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            "\u001b[?25hRequirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes==0.43.0) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch->bitsandbytes==0.43.0)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, bitsandbytes\n",
            "Successfully installed bitsandbytes-0.43.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105\n",
            "Collecting datasets==2.10.1\n",
            "  Downloading datasets-2.10.1-py3-none-any.whl (469 kB)\n",
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            "  Downloading dill-0.3.6-py3-none-any.whl (110 kB)\n",
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            "Collecting xxhash (from datasets==2.10.1)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hCollecting multiprocess (from datasets==2.10.1)\n",
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            "Collecting responses<0.19 (from datasets==2.10.1)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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            "INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n",
            "Collecting multiprocess (from datasets==2.10.1)\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25h  Downloading multiprocess-0.70.14-py310-none-any.whl (134 kB)\n",
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            "Installing collected packages: xxhash, dill, responses, multiprocess, datasets\n",
            "Successfully installed datasets-2.10.1 dill-0.3.6 multiprocess-0.70.14 responses-0.18.0 xxhash-3.4.1\n",
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            "Collecting peft==0.9.0\n",
            "  Downloading peft-0.9.0-py3-none-any.whl (190 kB)\n",
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            "  Downloading accelerate-0.28.0-py3-none-any.whl (290 kB)\n",
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            "Installing collected packages: accelerate, peft\n",
            "Successfully installed accelerate-0.28.0 peft-0.9.0\n",
            "Requirement already satisfied: sentencepiece==0.1.99 in /usr/local/lib/python3.10/dist-packages (0.1.99)\n",
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            "Collecting colorama==0.4.6\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Installing collected packages: colorama\n",
            "Successfully installed colorama-0.4.6\n"
          ]
        }
      ],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "!pip install bitsandbytes==0.43.0\n",
        "!pip install datasets==2.10.1\n",
        "!pip install transformers==4.38.2\n",
        "!pip install peft==0.9.0\n",
        "!pip install sentencepiece==0.1.99\n",
        "!pip install -U accelerate==0.28.0\n",
        "!pip install colorama==0.4.6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OMmuIblEXiU"
      },
      "source": [
        "The following code block takes about **20** seconds to run, but it may vary depending on the condition of Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZVVG_SQrvFpe"
      },
      "outputs": [],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "import os\n",
        "import sys\n",
        "import argparse\n",
        "import json\n",
        "import warnings\n",
        "import logging\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from datasets import load_dataset, load_from_disk\n",
        "import transformers, datasets\n",
        "from peft import PeftModel\n",
        "from colorama import *\n",
        "\n",
        "from tqdm import tqdm\n",
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "from transformers import GenerationConfig\n",
        "from peft import (\n",
        "    prepare_model_for_int8_training,\n",
        "    LoraConfig,\n",
        "    get_peft_model,\n",
        "    get_peft_model_state_dict,\n",
        "    prepare_model_for_kbit_training\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCo1znQhpBdt"
      },
      "source": [
        "## Download Dataset for Fine-tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7AI9jvvGtav2",
        "outputId": "467d77ec-58a2-422d-810a-f56ce94480ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'GenAI-Hw5'...\n",
            "remote: Enumerating objects: 35, done.\u001b[K\n",
            "remote: Counting objects: 100% (35/35), done.\u001b[K\n",
            "remote: Compressing objects: 100% (26/26), done.\u001b[K\n",
            "remote: Total 35 (delta 13), reused 25 (delta 7), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (35/35), 3.68 MiB | 12.77 MiB/s, done.\n",
            "Resolving deltas: 100% (13/13), done.\n"
          ]
        }
      ],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "# Download Training dataset\n",
        "# reference:https://github.com/chinese-poetry/chinese-poetry/tree/master/%E5%85%A8%E5%94%90%E8%AF%97?fbclid=IwAR2bM14S42T-VtrvMi3wywCqKfYJraBtMl7QVTo0qyPMjX9jj9Vj3JepFBA\n",
        "!git clone https://github.com/CheeEn-Yu/GenAI-Hw5.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ESzwsaT9poC4"
      },
      "source": [
        "## Fix Random Seeds\n",
        "There may be some randomness involved in the fine-tuning process. We fix random seeds to make the result reproducible."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "37Ra-OARteHI"
      },
      "outputs": [],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "seed = 42\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = False\n",
        "torch.manual_seed(seed)\n",
        "if torch.cuda.is_available():\n",
        "    torch.cuda.manual_seed_all(seed)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTaVpMgzp3oC"
      },
      "source": [
        "## Define Some Useful Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dKjoLO3xtfM1"
      },
      "outputs": [],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "# 生成訓練資料\n",
        "def generate_training_data(data_point):\n",
        "    \"\"\"\n",
        "    (1) Goal:\n",
        "        - This function is used to transform a data point (input and output texts) to tokens that our model can read\n",
        "\n",
        "    (2) Arguments:\n",
        "        - data_point: dict, with field \"instruction\", \"input\", and \"output\" which are all str\n",
        "\n",
        "    (3) Returns:\n",
        "        - a dict with model's input tokens, attention mask that make our model causal, and corresponding output targets\n",
        "\n",
        "    (3) Example:\n",
        "        - If you construct a dict, data_point_1, with field \"instruction\", \"input\", and \"output\" which are all str, you can use the function like this:\n",
        "            formulate_article(data_point_1)\n",
        "\n",
        "    \"\"\"\n",
        "    # construct full input prompt\n",
        "    prompt = f\"\"\"\\\n",
        "[INST] <<SYS>>\n",
        "You are a helpful assistant and good at writing Tang poem. 你是一個樂於助人的助手且擅長寫唐詩。\n",
        "<</SYS>>\n",
        "\n",
        "{data_point[\"instruction\"]}\n",
        "{data_point[\"input\"]}\n",
        "[/INST]\"\"\"\n",
        "    # count the number of input tokens\n",
        "    len_user_prompt_tokens = (\n",
        "        len(\n",
        "            tokenizer(\n",
        "                prompt,\n",
        "                truncation=True,\n",
        "                max_length=CUTOFF_LEN + 1,\n",
        "                padding=\"max_length\",\n",
        "            )[\"input_ids\"]\n",
        "        ) - 1\n",
        "    )\n",
        "    # transform input prompt into tokens\n",
        "    full_tokens = tokenizer(\n",
        "        prompt + \" \" + data_point[\"output\"] + \"</s>\",\n",
        "        truncation=True,\n",
        "        max_length=CUTOFF_LEN + 1,\n",
        "        padding=\"max_length\",\n",
        "    )[\"input_ids\"][:-1]\n",
        "    return {\n",
        "        \"input_ids\": full_tokens,\n",
        "        \"labels\": [-100] * len_user_prompt_tokens\n",
        "        + full_tokens[len_user_prompt_tokens:],\n",
        "        \"attention_mask\": [1] * (len(full_tokens)),\n",
        "    }\n",
        "\n",
        "# 進行生成回覆的評估\n",
        "def evaluate(instruction, generation_config, max_len, input=\"\", verbose=True):\n",
        "    \"\"\"\n",
        "    (1) Goal:\n",
        "        - This function is used to get the model's output given input strings\n",
        "\n",
        "    (2) Arguments:\n",
        "        - instruction: str, description of what you want model to do\n",
        "        - generation_config: transformers.GenerationConfig object, to specify decoding parameters relating to model inference\n",
        "        - max_len: int, max length of model's output\n",
        "        - input: str, input string the model needs to solve the instruction, default is \"\" (no input)\n",
        "        - verbose: bool, whether to print the mode's output, default is True\n",
        "\n",
        "    (3) Returns:\n",
        "        - output: str, the mode's response according to the instruction and the input\n",
        "\n",
        "    (3) Example:\n",
        "        - If you the instruction is \"ABC\" and the input is \"DEF\" and you want model to give an answer under 128 tokens, you can use the function like this:\n",
        "            evaluate(instruction=\"ABC\", generation_config=generation_config, max_len=128, input=\"DEF\")\n",
        "\n",
        "    \"\"\"\n",
        "    # construct full input prompt\n",
        "    prompt = f\"\"\"\\\n",
        "[INST] <<SYS>>\n",
        "You are a helpful assistant and good at writing Tang poem. 你是一個樂於助人的助手且擅長寫唐詩。\n",
        "<</SYS>>\n",
        "\n",
        "{instruction}\n",
        "{input}\n",
        "[/INST]\"\"\"\n",
        "    # 將提示文本轉換為模型所需的數字表示形式\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"].cuda()\n",
        "    # 使用模型進行生成回覆\n",
        "    generation_output = model.generate(\n",
        "        input_ids=input_ids,\n",
        "        generation_config=generation_config,\n",
        "        return_dict_in_generate=True,\n",
        "        output_scores=True,\n",
        "        max_new_tokens=max_len,\n",
        "    )\n",
        "    # 將生成的回覆解碼並印出\n",
        "    for s in generation_output.sequences:\n",
        "        output = tokenizer.decode(s)\n",
        "        output = output.split(\"[/INST]\")[1].replace(\"</s>\", \"\").replace(\"<s>\", \"\").replace(\"Assistant:\", \"\").replace(\"Assistant\", \"\").strip()\n",
        "        if (verbose):\n",
        "            print(output)\n",
        "\n",
        "    return output\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNxuuclCqFf5"
      },
      "source": [
        "## Download model and inference before fine-tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following code block takes about **10** minutes to run if you use the default setting, but it may vary depending on the condition of Colab."
      ],
      "metadata": {
        "id": "qmfEFM7TNuRC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" You may want (but not necessarily need) to change the LLM model \"\"\"\n",
        "\n",
        "model_name = \"/content/taide_7b\"                            # 設定想要用來進行fine-tune的模型，預設是使用TAIDE 7B的模型\n",
        "#model_name = \"MediaTek-Research/Breeze-7B-Instruct-v0_1\"   # 若想選擇使用MediaTek Breeze 7B的模型，可以將這行最前面的 \"#\" 刪除，並把底下 \"!\" 開頭的兩行刪除\n",
        "\n",
        "!wget -O taide_7b.zip \"https://www.dropbox.com/scl/fi/rlzdoidgejt9wiox88gz5/taide_7b.zip?rlkey=vtuc8xq7mq2d0dpuijxlb3jfj&dl=0\"\n",
        "# !gdown 1OiO5N9-wXDPX9XuGnPP-cgOoyFy4p3FD # 如果上面的連結失效（無法下載模型），可以將這行最前面的 \"#\" 刪除，即可正常下載\n",
        "!unzip taide_7b.zip"
      ],
      "metadata": {
        "id": "stY76riONxSz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce0908c2-17f6-4ebb-bcee-be0e3dafb970"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-28 09:39:10--  https://www.dropbox.com/scl/fi/rlzdoidgejt9wiox88gz5/taide_7b.zip?rlkey=vtuc8xq7mq2d0dpuijxlb3jfj&dl=0\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.81.18, 2620:100:6025:18::a27d:4512\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.81.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uc0963c8545712a82b3a32acc759.dl.dropboxusercontent.com/cd/0/inline/CP_jGT6xK1SY60yJAMSxE885w138SwvWu-9ZVM0CCpfpFORZlSwVj-e1xm3aqfaPc25haz7haJRiOIeduyx-wgxMwPzvypeDGgRhQvIQgCBu3XuOeghTM1vfXTwJ0qs3Vl3Yzx3_aT8ywzMtocFwQ_0K/file# [following]\n",
            "--2024-03-28 09:39:11--  https://uc0963c8545712a82b3a32acc759.dl.dropboxusercontent.com/cd/0/inline/CP_jGT6xK1SY60yJAMSxE885w138SwvWu-9ZVM0CCpfpFORZlSwVj-e1xm3aqfaPc25haz7haJRiOIeduyx-wgxMwPzvypeDGgRhQvIQgCBu3XuOeghTM1vfXTwJ0qs3Vl3Yzx3_aT8ywzMtocFwQ_0K/file\n",
            "Resolving uc0963c8545712a82b3a32acc759.dl.dropboxusercontent.com (uc0963c8545712a82b3a32acc759.dl.dropboxusercontent.com)... 162.125.81.15, 2620:100:6031:15::a27d:510f\n",
            "Connecting to uc0963c8545712a82b3a32acc759.dl.dropboxusercontent.com (uc0963c8545712a82b3a32acc759.dl.dropboxusercontent.com)|162.125.81.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /cd/0/inline2/CP_ex_RiHc23HcNdqoPt99TdgYB-D0gqU9ibRn4Indg3bh8-DKrTElkdStve_tGwjWG1AgdOxDHRea6RKClRAUFqsrwLGu8DPF6OAQc4B950zikSvxaYLM3qa5ntfYW91Vg3x7f8iV4PkvvsZN1NnNzUdvnNGGx8l3gS-9ighWBrPpM8orCB6iPmDq5ce4FyQiBPo40CIcej9naKA-Baj3Z4zhF2BuJ67gMcZKjyP0GLMo3pVUb8CjqLGed_pmFvH53q8b4WeJgvAMk9ncJmY4q-QNIQ79NGiS9xPUfYj99vPrEtk2EC1jIB2jrDuvn0N8AEvy5DDVhrGwpNZC6m70wfwv0lkutQn1fQ1e_vZXznUhh3jDkTWOWmCPT2MNkubL8/file [following]\n",
            "--2024-03-28 09:39:11--  https://uc0963c8545712a82b3a32acc759.dl.dropboxusercontent.com/cd/0/inline2/CP_ex_RiHc23HcNdqoPt99TdgYB-D0gqU9ibRn4Indg3bh8-DKrTElkdStve_tGwjWG1AgdOxDHRea6RKClRAUFqsrwLGu8DPF6OAQc4B950zikSvxaYLM3qa5ntfYW91Vg3x7f8iV4PkvvsZN1NnNzUdvnNGGx8l3gS-9ighWBrPpM8orCB6iPmDq5ce4FyQiBPo40CIcej9naKA-Baj3Z4zhF2BuJ67gMcZKjyP0GLMo3pVUb8CjqLGed_pmFvH53q8b4WeJgvAMk9ncJmY4q-QNIQ79NGiS9xPUfYj99vPrEtk2EC1jIB2jrDuvn0N8AEvy5DDVhrGwpNZC6m70wfwv0lkutQn1fQ1e_vZXznUhh3jDkTWOWmCPT2MNkubL8/file\n",
            "Reusing existing connection to uc0963c8545712a82b3a32acc759.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10688322515 (10.0G) [application/zip]\n",
            "Saving to: ‘taide_7b.zip’\n",
            "\n",
            "taide_7b.zip        100%[===================>]   9.95G  11.1MB/s    in 12m 29s \n",
            "\n",
            "2024-03-28 09:51:40 (13.6 MB/s) - ‘taide_7b.zip’ saved [10688322515/10688322515]\n",
            "\n",
            "Archive:  taide_7b.zip\n",
            "  inflating: taide_7b/config.json    \n",
            "  inflating: taide_7b/generation_config.json  \n",
            "  inflating: taide_7b/gitattributes  \n",
            "  inflating: taide_7b/model.safetensors  \n",
            " extracting: taide_7b/README.md      \n",
            "  inflating: taide_7b/special_tokens_map.json  \n",
            "  inflating: taide_7b/tokenizer.model  \n",
            "  inflating: taide_7b/tokenizer_config.json  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DFq6WXaBnxYq"
      },
      "source": [
        "## Inference before Fine-tuning\n",
        "Let's first see what our model can do without fine-tuning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ox5QRpz4NiPg"
      },
      "source": [
        "The following code block takes about **2** minutes to run if you use the default setting, but it may vary depending on the condition of Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e1sFQbHGn3Bw"
      },
      "outputs": [],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "cache_dir = \"./cache\"\n",
        "\n",
        "nf4_config = BitsAndBytesConfig(\n",
        "   load_in_4bit=True,\n",
        "   bnb_4bit_quant_type=\"nf4\",\n",
        "   bnb_4bit_use_double_quant=True,\n",
        "   bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "# 從指定的模型名稱或路徑載入預訓練的語言模型\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    cache_dir=cache_dir,\n",
        "    quantization_config=nf4_config,\n",
        "    low_cpu_mem_usage = True\n",
        ")\n",
        "\n",
        "# 創建 tokenizer 並設定結束符號 (eos_token)\n",
        "logging.getLogger('transformers').setLevel(logging.ERROR)\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    model_name,\n",
        "    add_eos_token=True,\n",
        "    cache_dir=cache_dir,\n",
        "    quantization_config=nf4_config\n",
        ")\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# 設定模型推理時需要用到的decoding parameters\n",
        "max_len = 128\n",
        "generation_config = GenerationConfig(\n",
        "    do_sample=True,\n",
        "    temperature=0.1,\n",
        "    num_beams=1,\n",
        "    top_p=0.3,\n",
        "    no_repeat_ngram_size=3,\n",
        "    pad_token_id=2,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0LyTYIcDOAkO"
      },
      "source": [
        "The following code block takes about **1** minutes to run if you use the default setting, but it may vary depending on the condition of Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJyuoPoO2TCr",
        "outputId": "47c872d9-6954-4345-d082-cccd5faec3c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Example 1:\n",
            "模型輸入:\n",
            "以下是一首唐詩的第一句話，請用你的知識判斷並完成整首詩。相見時難別亦難，東風無力百花殘。\n",
            "\n",
            "模型輸出:\n",
            "這首唐詩的第一句話是「相見 time 難別也難」，意思是「見面時困難，分別也困難」。以下是整首唐詩人歌：\n",
            "\n",
            "相逢時難，別離亦難。\n",
            "東風吹散，百花飄零。\n",
            "\n",
            "春日將盡，流水無情。\n",
            "離歌一曲，愁雲慘霧。\n",
            "此情可待，天涯共月光。\n",
            "但願人長久，千里共嬋娟。\n",
            "月光皎潔，清風徐徐。\n",
            "佳人相伴，不愁不\n",
            "--------------------------------------------------------------------------------\n",
            "Example 2:\n",
            "模型輸入:\n",
            "以下是一首唐詩的第一句話，請用你的知識判斷並完成整首詩。重帷深下莫愁堂，臥後清宵細細長。\n",
            "\n",
            "模型輸出:\n",
            "這首唐詩是李白的《清夜思》，內容描述了作者在夜晚沉思時對愛情的思念與渴望。\n",
            "\n",
            "原文：\n",
            "\n",
            "重幃深下，莫愁門外，清夜悠悠，思念綿綿。\n",
            "臥後淸宵，細細溫存，長長的夜，伴著我入眠。\n",
            "在重幃下的夜晚，不用擔心門外的風雨，因為愛人在身邊，一起分享著清冷的夜色，思慮著彼此的感情，這種情景可謂是甜蜜而溫馨的。而「細細」\n",
            "--------------------------------------------------------------------------------\n",
            "Example 3:\n",
            "模型輸入:\n",
            "以下是一首唐詩的第一句話，請用你的知識判斷並完成整首詩。芳辰追逸趣，禁苑信多奇。\n",
            "\n",
            "模型輸出:\n",
            "芳晨追尋逸趣, 禁苑中信多竒。\n",
            "\n",
            "這首唐詩的第一句話是「芳辰復追逸情，禁掖中信更多奇」。其中「芳晨」指的是美好時光，「追」意為追求，「逸情」指享受休閒娛樂的情景，「禁苑」則是指宮廷園林中的秘境。此詩透過描寫宮廷生活，表達出作者對於美好時光的珍惜與對自然美景的讚嘆。\n",
            "以下是這首唐詩人的完整作品：\n",
            "\n",
            "芳日追尋，逸情難捨\n",
            "--------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "# demo examples\n",
        "test_tang_list = ['相見時難別亦難，東風無力百花殘。', '重帷深下莫愁堂，臥後清宵細細長。', '芳辰追逸趣，禁苑信多奇。']\n",
        "\n",
        "# get the model output for each examples\n",
        "demo_before_finetune = []\n",
        "for tang in test_tang_list:\n",
        "  demo_before_finetune.append(f'模型輸入:\\n以下是一首唐詩的第一句話，請用你的知識判斷並完成整首詩。{tang}\\n\\n模型輸出:\\n'+evaluate('以下是一首唐詩的第一句話，請用你的知識判斷並完成整首詩。', generation_config, max_len, tang, verbose = False))\n",
        "\n",
        "# print and store the output to text file\n",
        "for idx in range(len(demo_before_finetune)):\n",
        "  print(f\"Example {idx + 1}:\")\n",
        "  print(demo_before_finetune[idx])\n",
        "  print(\"-\" * 80)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Stf_U-9FqPjZ"
      },
      "source": [
        "## Set Hyperarameters for Fine-tuning\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" It is highly recommended you try to play around this hyperparameter \"\"\"\n",
        "\n",
        "num_train_data = 1040 # 設定用來訓練的資料數量，可設置的最大值為5000。在大部分情況下會希望訓練資料盡量越多越好，這會讓模型看過更多樣化的詩句，進而提升生成品質，但是也會增加訓練的時間\n",
        "                      # 使用預設參數(1040): fine-tuning大約需要25分鐘，完整跑完所有cell大約需要50分鐘\n",
        "                      # 使用最大值(5000): fine-tuning大約需要100分鐘，完整跑完所有cell大約需要120分鐘"
      ],
      "metadata": {
        "id": "Q2ilhBGhTDtU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c1fbknrxXu3C"
      },
      "outputs": [],
      "source": [
        "\"\"\" You may want (but not necessarily need) to change some of these hyperparameters \"\"\"\n",
        "\n",
        "output_dir = \"/content/drive/MyDrive\"  # 設定作業結果輸出目錄 (如果想要把作業結果存在其他目錄底下可以修改這裡，強烈建議存在預設值的子目錄下，也就是Google Drive裡)\n",
        "ckpt_dir = \"./exp1\" # 設定model checkpoint儲存目錄 (如果想要將model checkpoints存在其他目錄下可以修改這裡)\n",
        "num_epoch = 1  # 設定訓練的總Epoch數 (數字越高，訓練越久，若使用免費版的colab需要注意訓練太久可能會斷線)\n",
        "LEARNING_RATE = 3e-4  # 設定學習率"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oorrZOKpthsm"
      },
      "outputs": [],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "cache_dir = \"./cache\"  # 設定快取目錄路徑\n",
        "from_ckpt = False  # 是否從checkpoint載入模型的權重，預設為否\n",
        "ckpt_name = None  # 從特定checkpoint載入權重時使用的檔案名稱，預設為無\n",
        "dataset_dir = \"./GenAI-Hw5/Tang_training_data.json\"  # 設定資料集的目錄或檔案路徑\n",
        "logging_steps = 20  # 定義訓練過程中每隔多少步驟輸出一次訓練誌\n",
        "save_steps = 65  # 定義訓練過程中每隔多少步驟保存一次模型\n",
        "save_total_limit = 3  # 控制最多保留幾個模型checkpoint\n",
        "report_to = None  # 設定上報實驗指標的目標，預設為無\n",
        "MICRO_BATCH_SIZE = 4  # 定義微批次的大小\n",
        "BATCH_SIZE = 16  # 定義一個批次的大小\n",
        "GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE  # 計算每個微批次累積的梯度步數\n",
        "CUTOFF_LEN = 256  # 設定文本截斷的最大長度\n",
        "LORA_R = 8  # 設定LORA（Layer-wise Random Attention）的R值\n",
        "LORA_ALPHA = 16  # 設定LORA的Alpha值\n",
        "LORA_DROPOUT = 0.05  # 設定LORA的Dropout率\n",
        "VAL_SET_SIZE = 0  # 設定驗證集的大小，預設為無\n",
        "TARGET_MODULES = [\"q_proj\", \"up_proj\", \"o_proj\", \"k_proj\", \"down_proj\", \"gate_proj\", \"v_proj\"] # 設定目標模組，這些模組的權重將被保存為checkpoint\n",
        "device_map = \"auto\"  # 設定設備映射，預設為\"auto\"\n",
        "world_size = int(os.environ.get(\"WORLD_SIZE\", 1))  # 獲取環境變數\"WORLD_SIZE\"的值，若未設定則預設為1\n",
        "ddp = world_size != 1  # 根據world_size判斷是否使用分散式數據處理(DDP)，若world_size為1則不使用DDP\n",
        "if ddp:\n",
        "    device_map = {\"\": int(os.environ.get(\"LOCAL_RANK\") or 0)}\n",
        "    GRADIENT_ACCUMULATION_STEPS = GRADIENT_ACCUMULATION_STEPS // world_size"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Start Fine-tuning"
      ],
      "metadata": {
        "id": "REMmOD6L4tp9"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RkOt2hDhR4lG"
      },
      "source": [
        "The following code block takes about **25** minutes to run if you use the default setting, but it may vary depending on the condition of Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "referenced_widgets": [
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            "42b470f4cb6b430e986cde2a732ae1bf",
            "ce57d1252b2043138ddfaff3823fe283",
            "07d88c90cec640f3ae6c19a46be81d4b",
            "67bcf0c9b6bf4b18a5a318ce60f5b544",
            "895ad55bc1074f6cbbbcb4174e9bc0cd",
            "f75b54c99e4d446e8a9701d98a4eaff7",
            "01abe8edec2c406d81d13640c47af819",
            "b325f6ee7f964b57947f3fa936ef5bf4",
            "673946a2e8f84e62b4021faa5f80dd54",
            "7d3e1029aeb54fb5ac54c045df577bd8",
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            "b038219c15774af6b67782d33545b294",
            "3dd1186fcd6142b6b025c1e38c6c6430",
            "2b3f31bf14bb4468a783458a3a094ab2",
            "8c31778d327a4fd68cc66aee90e74c4e",
            "8ea6414f11b440b694b236c8fb5cda61",
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            "2374eb31be5b4d7f8f607844b3effedc",
            "a254416d90f4439eaf64ea9dc4e09cf5",
            "876a4f1972ac4206a13d57142a850128",
            "f08d706b3ef9486383d12ad5d37bd622",
            "c34204d7afd74e2fbb9f31082ae759ec",
            "2869cbfa8c69460785221af61c23561c",
            "421d83680d714ae19eb3ffef4175d9f2",
            "db50cca2cf90410fb8b2c643c760a4d4",
            "470c01d48ee9431d87257020d8f0bef3",
            "be54503f2793416a8d6321a28983c31d",
            "ad1e879ea0f64871839ee6b3bda0882c",
            "377a892ebfb74b6faf3ba364df2c3c79",
            "13b547710721495d9208e57ada20c5f5",
            "1e4c8bbbc92a459b953fe741be83d132",
            "a72fdbc5e3ce40fd88e7135bcdfb9ee8",
            "a2a19d258b9e419cbb22fe5934efdf64",
            "12eaca9babad41aca6d282efd99eb871",
            "c54cb4ab8b0640c3b53367fb51d53075",
            "08a016858a4f444bab67bf60e2ac7363",
            "3a4a22f41304444c9f10854cc60e3ae0",
            "4192754827f24c0eb33a4e8f86ab94e5",
            "1e66e0ceab89438386d8c1aa494af916",
            "d16cf8c1424043f0b2734dacecf1165e",
            "2f673c45b2e24ea2a6cf73b2fe4c53cf",
            "45b5098d78c84bc2a3596249737be3fa",
            "ca6c4246f63e49e895bab9d6fcf04cdb",
            "78462ffc2b7941a5a76dc50db0b54d52"
          ],
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "6W-xe7h9ti0x",
        "outputId": "de38b13d-a039-4227-9893-e5985796ee23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-24f577c310f5f72c/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\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": "c82f4fa9451a43f2bf0dae4f29eb9a5e"
            }
          },
          "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": "01abe8edec2c406d81d13640c47af819"
            }
          },
          "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": "31fcea3231644a9abcc196aa0ca1ae6b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-24f577c310f5f72c/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2869cbfa8c69460785221af61c23561c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1040 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "12eaca9babad41aca6d282efd99eb871"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='65' max='65' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [65/65 18:15, Epoch 1/1]\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>20</td>\n",
              "      <td>4.475700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.761600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.415700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " If there's a warning about missing keys above, please disregard :)\n"
          ]
        }
      ],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "# create the output directory you specify\n",
        "os.makedirs(output_dir, exist_ok = True)\n",
        "os.makedirs(ckpt_dir, exist_ok = True)\n",
        "\n",
        "# 根據 from_ckpt 標誌，從 checkpoint 載入模型權重\n",
        "if from_ckpt:\n",
        "    model = PeftModel.from_pretrained(model, ckpt_name)\n",
        "\n",
        "# 將模型準備好以使用 INT8 訓練\n",
        "model = prepare_model_for_int8_training(model)\n",
        "\n",
        "# 使用 LoraConfig 配置 LORA 模型\n",
        "config = LoraConfig(\n",
        "    r=LORA_R,\n",
        "    lora_alpha=LORA_ALPHA,\n",
        "    target_modules=TARGET_MODULES,\n",
        "    lora_dropout=LORA_DROPOUT,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "model = get_peft_model(model, config)\n",
        "\n",
        "# 將 tokenizer 的 padding token 設定為 0\n",
        "tokenizer.pad_token_id = 0\n",
        "\n",
        "# 載入並處理訓練數據\n",
        "with open(dataset_dir, \"r\", encoding = \"utf-8\") as f:\n",
        "    data_json = json.load(f)\n",
        "with open(\"tmp_dataset.json\", \"w\", encoding = \"utf-8\") as f:\n",
        "    json.dump(data_json[:num_train_data], f, indent = 2, ensure_ascii = False)\n",
        "\n",
        "data = load_dataset('json', data_files=\"tmp_dataset.json\", download_mode=\"force_redownload\")\n",
        "\n",
        "# 將訓練數據分為訓練集和驗證集（若 VAL_SET_SIZE 大於 0）\n",
        "if VAL_SET_SIZE > 0:\n",
        "    train_val = data[\"train\"].train_test_split(\n",
        "        test_size=VAL_SET_SIZE, shuffle=True, seed=42\n",
        "    )\n",
        "    train_data = train_val[\"train\"].shuffle().map(generate_training_data)\n",
        "    val_data = train_val[\"test\"].shuffle().map(generate_training_data)\n",
        "else:\n",
        "    train_data = data['train'].shuffle().map(generate_training_data)\n",
        "    val_data = None\n",
        "\n",
        "# 使用 Transformers Trainer 進行模型訓練\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=train_data,\n",
        "    eval_dataset=val_data,\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=MICRO_BATCH_SIZE,\n",
        "        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
        "        warmup_steps=50,\n",
        "        num_train_epochs=num_epoch,\n",
        "        learning_rate=LEARNING_RATE,\n",
        "        fp16=True,  # 使用混合精度訓練\n",
        "        logging_steps=logging_steps,\n",
        "        save_strategy=\"steps\",\n",
        "        save_steps=save_steps,\n",
        "        output_dir=ckpt_dir,\n",
        "        save_total_limit=save_total_limit,\n",
        "        ddp_find_unused_parameters=False if ddp else None,  # 是否使用 DDP，控制梯度更新策略\n",
        "        report_to=report_to,\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "\n",
        "# 禁用模型的 cache 功能\n",
        "model.config.use_cache = False\n",
        "\n",
        "# 若使用 PyTorch 2.0 版本以上且非 Windows 系統，進行模型編譯\n",
        "if torch.__version__ >= \"2\" and sys.platform != 'win32':\n",
        "    model = torch.compile(model)\n",
        "\n",
        "# 開始模型訓練\n",
        "trainer.train()\n",
        "\n",
        "# 將訓練完的模型保存到指定的目錄中\n",
        "model.save_pretrained(ckpt_dir)\n",
        "\n",
        "# 印出訓練過程中可能的缺失權重的警告信息\n",
        "print(\"\\n If there's a warning about missing keys above, please disregard :)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKeGb8bRqWux"
      },
      "source": [
        "##  Testing\n",
        "The fine-tuning process is done. We then want to test whether our model can do the task that we wanted it to do before but failed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8I3stApnTWb"
      },
      "source": [
        "We need to first load the fine-tuned model for checkpoint we saved."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ag6GvOCe9Ql",
        "outputId": "11d9bb8e-b6c8-4ee1-e905-28e259b6f6a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "all available checkpoints:\n",
            " id: checkpoint name\n",
            "  0: checkpoint-65\n"
          ]
        }
      ],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "# find all available checkpoints\n",
        "ckpts = []\n",
        "for ckpt in os.listdir(ckpt_dir):\n",
        "    if (ckpt.startswith(\"checkpoint-\")):\n",
        "        ckpts.append(ckpt)\n",
        "\n",
        "# list all the checkpoints\n",
        "ckpts = sorted(ckpts, key = lambda ckpt: int(ckpt.split(\"-\")[-1]))\n",
        "print(\"all available checkpoints:\")\n",
        "print(\" id: checkpoint name\")\n",
        "for (i, ckpt) in enumerate(ckpts):\n",
        "    print(f\"{i:>3}: {ckpt}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "khq-LbNlcdfp"
      },
      "outputs": [],
      "source": [
        "\"\"\" You may want (but not necessarily need) to change the check point \"\"\"\n",
        "\n",
        "id_of_ckpt_to_use = -1  # 要用來進行推理的checkpoint的id(對應上一個cell的輸出結果)\n",
        "                        # 預設值-1指的是上列checkpoints中的\"倒數\"第一個，也就是最後一個checkpoint\n",
        "                        # 如果想要選擇其他checkpoint，可以把-1改成有列出的checkpoint id中的其中一個\n",
        "\n",
        "ckpt_name = os.path.join(ckpt_dir, ckpts[id_of_ckpt_to_use])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vdO5i3X3bTus"
      },
      "outputs": [],
      "source": [
        "\"\"\" You may want (but not necessarily need) to change decoding parameters \"\"\"\n",
        "# 你可以在這裡調整decoding parameter，decoding parameter的詳細解釋請見homework slides\n",
        "max_len = 128   # 生成回復的最大長度\n",
        "temperature = 0.1  # 設定生成回覆的隨機度，值越小生成的回覆越穩定\n",
        "top_p = 0.3  # Top-p (nucleus) 抽樣的機率閾值，用於控制生成回覆的多樣性\n",
        "# top_k = 5 # 調整Top-k值，以增加生成回覆的多樣性和避免生成重複的詞彙"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTr0DfVBSekD"
      },
      "source": [
        "The following code block takes about **2** minutes to run if you use the default setting, but it may vary depending on the condition of Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-wKVPpMVtkql"
      },
      "outputs": [],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "test_data_path = \"GenAI-Hw5/Tang_testing_data.json\"\n",
        "output_path = os.path.join(output_dir, \"results.txt\")\n",
        "\n",
        "cache_dir = \"./cache\"  # 設定快取目錄路徑\n",
        "seed = 42  # 設定隨機種子，用於重現結果\n",
        "no_repeat_ngram_size = 3  # 設定禁止重複 Ngram 的大小，用於避免生成重複片段\n",
        "\n",
        "nf4_config = BitsAndBytesConfig(\n",
        "   load_in_4bit=True,\n",
        "   bnb_4bit_quant_type=\"nf4\",\n",
        "   bnb_4bit_use_double_quant=True,\n",
        "   bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "# 使用 tokenizer 將模型名稱轉換成模型可讀的數字表示形式\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    model_name,\n",
        "    cache_dir=cache_dir,\n",
        "    quantization_config=nf4_config\n",
        ")\n",
        "\n",
        "# 從預訓練模型載入模型並設定為 8 位整數 (INT8) 模型\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=nf4_config,\n",
        "    device_map={'': 0},  # 設定使用的設備，此處指定為 GPU 0\n",
        "    cache_dir=cache_dir\n",
        ")\n",
        "\n",
        "# 從指定的 checkpoint 載入模型權重\n",
        "model = PeftModel.from_pretrained(model, ckpt_name, device_map={'': 0})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vs4_EtnoWA5b"
      },
      "source": [
        "The following code block takes about **4** minutes to run if you use the default setting, but it may vary depending on the condition of Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xcHoU2X8xRkn"
      },
      "outputs": [],
      "source": [
        "\"\"\" It is recommmended NOT to change codes in this cell \"\"\"\n",
        "\n",
        "results = []\n",
        "\n",
        "# 設定生成配置，包括隨機度、束搜索等相關參數\n",
        "generation_config = GenerationConfig(\n",
        "    do_sample=True,\n",
        "    temperature=temperature,\n",
        "    num_beams=1,\n",
        "    top_p=top_p,\n",
        "    # top_k=top_k,\n",
        "    no_repeat_ngram_size=no_repeat_ngram_size,\n",
        "    pad_token_id=2\n",
        ")\n",
        "\n",
        "# 讀取測試資料\n",
        "with open(test_data_path, \"r\", encoding = \"utf-8\") as f:\n",
        "    test_datas = json.load(f)\n",
        "\n",
        "# 對於每個測試資料進行預測，並存下結果\n",
        "with open(output_path, \"w\", encoding = \"utf-8\") as f:\n",
        "  for (i, test_data) in enumerate(test_datas):\n",
        "      predict = evaluate(test_data[\"instruction\"], generation_config, max_len, test_data[\"input\"], verbose = False)\n",
        "      f.write(f\"{i+1}. \"+test_data[\"input\"]+predict+\"\\n\")\n",
        "      print(f\"{i+1}. \"+test_data[\"input\"]+predict)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **IMPORTANT**: Submit above 15 poems to DaVinci Assistant.\n",
        "You can find these poems in your \"results.txt\". The grading of this homework will be based on the evaluation results of DaVinci Assistant on these poems."
      ],
      "metadata": {
        "id": "1tJT1WINKXV7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## See how the fine-tune model do compared to model without fine-tuning"
      ],
      "metadata": {
        "id": "sT78LeWaJM4D"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tx_mZZAZWdiE"
      },
      "source": [
        "We now check what our model can do on the same examples we saw in the \"Inference before Fine-tuning\" section above.\n",
        "\n",
        "The following code block takes about **40** seconds to run if you use the default setting, but it may vary depending on the condition of Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BtDG2WhIWZlS",
        "outputId": "04870472-0897-4d24-8660-477c68025b34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Example 1:\n",
            "模型輸入:\n",
            "以下是一首唐詩的第一句話，請用你的知識判斷並完成整首詩。相見時難別亦難，東風無力百花殘。\n",
            "\n",
            "模型輸出:\n",
            "春日長安好，春風渭水寒。\n",
            "--------------------------------------------------------------------------------\n",
            "Example 2:\n",
            "模型輸入:\n",
            "以下是一首唐詩的第一句話，請用你的知識判斷並完成整首詩。重帷深下莫愁堂，臥後清宵細細長。\n",
            "\n",
            "模型輸出:\n",
            "月光如水流銀漢，風吹不卷翠羅裳。\n",
            "--------------------------------------------------------------------------------\n",
            "Example 3:\n",
            "模型輸入:\n",
            "以下是一首唐詩的第一句話，請用你的知識判斷並完成整首詩。芳辰追逸趣，禁苑信多奇。\n",
            "\n",
            "模型輸出:\n",
            "日影移花影，風聲帶鳥枝。\n",
            "--------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "# using the same demo examples as before\n",
        "test_tang_list = ['相見時難別亦難，東風無力百花殘。', '重帷深下莫愁堂，臥後清宵細細長。', '芳辰追逸趣，禁苑信多奇。']\n",
        "\n",
        "# inference our fine-tuned model\n",
        "demo_after_finetune = []\n",
        "for tang in test_tang_list:\n",
        "  demo_after_finetune.append(f'模型輸入:\\n以下是一首唐詩的第一句話，請用你的知識判斷並完成整首詩。{tang}\\n\\n模型輸出:\\n'+evaluate('以下是一首唐詩的第一句話，請用你的知識判斷並完成整首詩。', generation_config, max_len, tang, verbose = False))\n",
        "\n",
        "# print and store the output to text file\n",
        "for idx in range(len(demo_after_finetune)):\n",
        "  print(f\"Example {idx + 1}:\")\n",
        "  print(demo_after_finetune[idx])\n",
        "  print(\"-\" * 80)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **IMPORTANT**: DO NOT submit the above 3 examples to DaVinci Assistant.\n",
        "This 3 examples are only used for comparing how model peforms before and after fine-tuning\n"
      ],
      "metadata": {
        "id": "wagFeDX4Hpa8"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWgTS13bgnWQ"
      },
      "source": [
        "## Download Results\n",
        "You MUST have this file to finish your homework. If your browser does not download it automatically, you can find it in your Google Drive."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rUDuNQOY4os_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0d938a6b-7b3a-4a9d-f5c7-2d1fbd86d909"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_7a7b4b81-2f8a-4dcc-8c1c-2ef686bfaa13\", \"results.txt\", 1422)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.download(output_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reference\n",
        "\n",
        "[Tang Poem Dataset](https://github.com/chinese-poetry/chinese-poetry/tree/master/%E5%85%A8%E5%94%90%E8%AF%97?fbclid=IwAR2bM14S42T-VtrvMi3wywCqKfYJraBtMl7QVTo0qyPMjX9jj9Vj3JepFBA)"
      ],
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        "id": "PpkNFzvLz7iI"
      }
    }
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