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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hululuzhu/chinese-ai-writing-share/blob/main/training/2023_llm/2023_LLM_poem_ai_baichuan_finetune.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2023 LLM (baichuan-7b) 写诗 (WIP)\n",
        "- 基础模型 https://huggingface.co/baichuan-inc/Baichuan-7B\n",
        "- 数据来源 https://github.com/chinese-poetry/chinese-poetry\n",
        "- 微调参数\n",
        "  - 基础模型：int8 （7-8G GPU ram）\n",
        "  - 微调方法：LoRA （16rank）\n",
        "\n",
        "## 进程\n",
        "- 12/25/2023：第一稿，走通流程\n",
        "- 预计01/30/2024 发布LoRA到HuggingFace"
      ],
      "metadata": {
        "id": "rdQOP9DIvlBS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 准备阶段\n",
        "- GPU （10G RAM以上，所以T4也可以）\n",
        "- Transformer 需要特殊4.33.2版本，否则BaiChuan tokenizer报错，头痛\n"
      ],
      "metadata": {
        "id": "8kiLFQGQwem1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BF3H78atmZCB",
        "outputId": "21a1d402-d148-48f0-af09-89cfab52e9d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: NVIDIA A100-SXM4-40GB (UUID: GPU-7785eea3-91b0-570f-9fdc-44fc31f42918)\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi -L"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q nvidia-ml-py3\n",
        "import nvidia_smi\n",
        "nvidia_smi.nvmlInit()\n",
        "handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)\n",
        "# card id 0 hardcoded here, there is also a call to get all available card ids, so we could iterate\n",
        "info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)\n",
        "nvidia_smi.nvmlShutdown()\n",
        "\n",
        "print(\"Total memory:\", info.total)\n",
        "print(\"Free memory:\", info.free)\n",
        "print(\"Used memory:\", info.used)\n",
        "\n",
        "assert info.free > 1e10, (\n",
        "    \"Looks like your GPU is busy or not having enough 10G memory to continue\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1udc9ScGmhoC",
        "outputId": "ffe1b646-0608-423c-bb18-af67b11978d8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for nvidia-ml-py3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Total memory: 42949672960\n",
            "Free memory: 42479321088\n",
            "Used memory: 470351872\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q bitsandbytes\n",
        "!pip install -q datasets loralib sentencepiece\n",
        "!pip install -q peft\n",
        "# https://medium.com/@zhaozhiming/fine-tuning-large-language-models-with-llama-factory-34e288d3f381\n",
        "!pip install transformers==4.33.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-hGRLwNmlxf",
        "outputId": "a4becccd-2074-4084-bba2-24e5c91d8730"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.3/168.3 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.7/265.7 kB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting transformers==4.33.2\n",
            "  Downloading transformers-4.33.2-py3-none-any.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m57.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.2) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.2) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.2) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.2) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.2) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.2) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.2) (2.31.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers==4.33.2)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m100.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.2) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.2) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers==4.33.2) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers==4.33.2) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.33.2) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.33.2) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.33.2) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.33.2) (2023.11.17)\n",
            "Installing collected packages: tokenizers, transformers\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.15.0\n",
            "    Uninstalling tokenizers-0.15.0:\n",
            "      Successfully uninstalled tokenizers-0.15.0\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.35.2\n",
            "    Uninstalling transformers-4.35.2:\n",
            "      Successfully uninstalled transformers-4.35.2\n",
            "Successfully installed tokenizers-0.13.3 transformers-4.33.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# disable warnings unless needed\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "whAOEbXHmnZV"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# https://github.com/googlecolab/colabtools/issues/3409\n",
        "import locale\n",
        "# Required for handling chinese chars\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "rQJxgD7Zw4M6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import urllib.request\n",
        "import pandas as pd\n",
        "!pip install -q \"tqdm>=4.36.1\" > /tmp/na\n",
        "from tqdm.notebook import tqdm\n",
        "!pip install -q chinese-converter\n",
        "import chinese_converter  # 繁体到简体需要\n",
        "import pickle\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "GIKl51xfw6eL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset, load_dataset\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import pathlib\n",
        "from peft import PeftModel, get_peft_config, get_peft_model, LoraConfig, TaskType, prepare_model_for_int8_training\n",
        "import pickle\n",
        "import sys\n",
        "import torch\n",
        "import transformers\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, AutoModelForSeq2SeqLM, DataCollatorForLanguageModeling\n",
        ""
      ],
      "metadata": {
        "id": "8ERvE1QNoUkq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# int8方式导入BaiChuan 7B模型\n",
        "- 理论上可以int4，可以GPU内存减半"
      ],
      "metadata": {
        "id": "LhhLWSWSxKeu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# again, the special version of Transfromer lib above is required, annoying\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    \"baichuan-inc/Baichuan-7B\", trust_remote_code=True)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"baichuan-inc/Baichuan-7B\", device_map=\"auto\",\n",
        "    load_in_8bit=True,\n",
        "    trust_remote_code=True)"
      ],
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          "height": 486,
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            "4d4e83a2d4cd46a7a3d8adbeaed160a6",
            "e15fc957445b450e846ccdd024a02190",
            "1734838b85564c96a253b862ba8f99ba",
            "38a5e6655a524cd692e5ce48f6ed0e53",
            "1a1c3eb89eda4e06bdb34621b4569d7a",
            "9ba2a29e34b84381ab51171530afe40a",
            "a5b42b13ba884e639341e7146512457e",
            "1eb35108c6e0411294f187ccdfa4abd6"
          ]
        },
        "id": "oxy4T4lSmzTn",
        "outputId": "da25be8d-7eb7-4c6c-8f61-cb99b9402d34"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/802 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a099952d59843ca9340f69bf43ce671"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenization_baichuan.py:   0%|          | 0.00/9.57k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f0aa7a57f244102884b43227b4f3a74"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/baichuan-inc/Baichuan-7B:\n",
            "- tokenization_baichuan.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/1.14M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "66f2ca6c02ed4494ab03f222c64c4a1d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/411 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38ebc6f9643446bbb433c73043363cbc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/656 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db15609f9a0543a0aa283b14961bee2c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "configuration_baichuan.py:   0%|          | 0.00/2.35k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0db8ff5930624279997fe1346294a2ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/baichuan-inc/Baichuan-7B:\n",
            "- configuration_baichuan.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modeling_baichuan.py:   0%|          | 0.00/28.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be3c25d762f64e98878db50c208e3c43"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/baichuan-inc/Baichuan-7B:\n",
            "- modeling_baichuan.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/14.0G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd0ce1d08f5444af9af769e3ec89f80b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/132 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3252d2f9f50248a5b74d97bde514181d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 简单测试\n",
        "inputs = tokenizer('登鹳雀楼->王之涣\\n夜雨寄北->', return_tensors='pt')\n",
        "inputs = inputs.to('cuda:0')\n",
        "pred = model.generate(**inputs, max_new_tokens=64,repetition_penalty=1.1)\n",
        "print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9img3XUnGr9",
        "outputId": "2e87ca45-40af-4489-e2f5-7be1de76eeb4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "登鹳雀楼->王之涣\n",
            "夜雨寄北->李商隐\n",
            "过华清宫绝句三首(其一)->杜牧\n",
            "望岳>杜甫\n",
            "春望>>杜甫\n",
            "石壕吏-<<新唐书·元载传》\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 和之前Alpaca设置对应，但不一定正确\n",
        "tokenizer.pad_token_id = 0\n",
        "tokenizer.padding_side = \"left\"  # Allow batched inference"
      ],
      "metadata": {
        "id": "_A-12p8DpQgp"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 验证阶段，之后换为False全部数据导入\n",
        "IS_TEST_FLOW = True"
      ],
      "metadata": {
        "id": "UJc4vzQ2xhMs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# https://github.com/chinese-poetry/chinese-poetry, last update 04/18/2023\n",
        "POEM_CONTENT = {\n",
        "    'tang': {\n",
        "        'total': 58,\n",
        "        'pattern': \"https://raw.githubusercontent.com/chinese-poetry/chinese-poetry/master/%E5%85%A8%E5%94%90%E8%AF%97/poet.tang.{0}.json\"\n",
        "    },\n",
        "    'song': {\n",
        "        'total': 255,\n",
        "        'pattern': \"https://raw.githubusercontent.com/chinese-poetry/chinese-poetry/master/%E5%85%A8%E5%94%90%E8%AF%97/poet.song.{0}.json\"\n",
        "    }\n",
        "}\n",
        "\n",
        "def get_poems(is_test=True, verbose=True):\n",
        "  df_list = []\n",
        "  for dynasty in POEM_CONTENT:\n",
        "    size = 3 if is_test else POEM_CONTENT[dynasty]['total']\n",
        "    pbar = tqdm(total=size, desc=\"Dynasty \" + dynasty)\n",
        "    for i in range(size):\n",
        "      url = POEM_CONTENT[dynasty]['pattern'].format(i * 1000)\n",
        "      if verbose:\n",
        "        print(f\"download {url} now\")\n",
        "      df_list.append(pd.read_json(url))\n",
        "      pbar.update(1)\n",
        "  return pd.concat(df_list)"
      ],
      "metadata": {
        "id": "CaeCRIBNp4fF"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = get_poems(is_test=IS_TEST_FLOW, verbose=False)\n",
        "df['concat_paragraphs'] = [''.join(map(str, l)) for l in df['paragraphs']]\n",
        "df = df[['author', 'title', 'concat_paragraphs']]\n",
        "\n",
        "def convert_schinese(tchinese):\n",
        "  return chinese_converter.to_simplified(tchinese)\n",
        "\n",
        "df['s_content'] = df.apply(lambda row: convert_schinese(''.join(row.concat_paragraphs)), axis=1)\n",
        "df['s_title'] = df.apply(lambda row: convert_schinese(''.join(row.title)), axis=1)\n",
        "df['s_author'] = df.apply(lambda row: convert_schinese(''.join(row.author)), axis=1)\n",
        "\n",
        "my_df = df\n",
        "print(\"my_df size\", len(my_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99,
          "referenced_widgets": [
            "62acf20f824442ecb44dc73aed55fe74",
            "fb015e79a08e4190bfcf34f5d80faa38",
            "9990654f8b9d4456b0d5b45c2d6009d0",
            "0e15f69476944567a2d0ef7dcefc6d99",
            "c5ddbfc89fc44aac9000a88d41e13bc9",
            "d0479f2a01d44f528eafb4084499791f",
            "efdb0ebb30ef4f66a597856f84587690",
            "e0067fac33334f2a841b290a852caf81",
            "8d88321d2d0445d0a0095bf287e157b1",
            "6963dd393729422cba1fbb5ddb323dc3",
            "666701cfa143427eb82ee3afeb64b981",
            "54ab4dadce0f433485b1a43f236f1886",
            "3f6cf8eac2ff4b18bf5741d7e029a9b3",
            "02dc156e419f4c519f73818fa2ef4196",
            "b276832863fd4cec8f4607dbcac76aad",
            "7d0b41b3a5bd4282ba5fca2ecc451227",
            "b60b4b8ee7b44b389ac563600a7a4ee6",
            "dd302d5cf6274b358a27142cd6aa9c97",
            "14d4d8ccad2a4bff886e9381384ac7e3",
            "7005cbd05bbf4d3ea8ff5ed9c1129f9f",
            "fb9cf484ec77414e9dd17c4f45e3362e",
            "fac3565a33e64350a3ac5734123824b3"
          ]
        },
        "id": "Ob2YaIrhp9I9",
        "outputId": "f29ff9a4-fb3c-4993-9ec0-2fb7ae32e746"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dynasty tang:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62acf20f824442ecb44dc73aed55fe74"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dynasty song:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "54ab4dadce0f433485b1a43f236f1886"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_df size 6002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 之前同repo的一些数据清理代码\n",
        "MAX_AUTHOR_CHAR = 4\n",
        "MAX_TITLE_CHAR = 12\n",
        "MIN_CONTENT_CHAR = 20\n",
        "MAX_CONTENT_CHAR = 32\n",
        "BAD_TOKENS = \" ()[]《》（）□{}abcdefgxyz一\"\n",
        "\n",
        "def trim_author_fn(row):\n",
        "  return row.s_author[:MAX_AUTHOR_CHAR]\n",
        "\n",
        "def trim_title_fn(row):\n",
        "  trimed_title = row.s_title[:MAX_TITLE_CHAR]\n",
        "  for b in BAD_TOKENS:\n",
        "    trimed_title = trimed_title.replace(b, \"\")\n",
        "  return trimed_title\n",
        "\n",
        "def trim_content_fn(row):\n",
        "  trimed_content = row.s_content[:MAX_CONTENT_CHAR]\n",
        "  # # End with a period to avoid partial ending to confuse model\n",
        "  for b in BAD_TOKENS:\n",
        "    trimed_content = trimed_content.replace(b, \"\")\n",
        "  last_period = trimed_content.rfind(\"。\")\n",
        "  return trimed_content[:last_period+1]\n",
        "  # return trimed_content\n",
        "\n",
        "# Trim the size, a soft copy to avoid the view/copy conflict warning\n",
        "my_df['s_author_trim'] = my_df.copy().apply(trim_author_fn, axis=1)\n",
        "my_df['s_title_trim'] = my_df.copy().apply(trim_title_fn, axis=1)\n",
        "my_df['s_content_trim'] = my_df.copy().apply(trim_content_fn, axis=1)\n",
        "\n",
        "print(\"my_df size\", len(my_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-fsFcYPeqCOG",
        "outputId": "b5f8ed2a-47df-47fc-c0e4-7251f48b922c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_df size 6002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Title cannot be empty\n",
        "empty_title_mask = (my_df['s_title_trim'].str.len() == 0)\n",
        "too_short_cotent_mask = (my_df['s_content_trim'].str.len() <= MIN_CONTENT_CHAR)\n",
        "invalid_mask = (('无正文' == my_df['s_content_trim']) | ('无正文' == my_df['s_author_trim']))\n",
        "too_short_mask =  empty_title_mask | too_short_cotent_mask | invalid_mask\n",
        "# filtered_my_df = my_df.loc[too_short_mask]\n",
        "# filtered_my_df\n",
        "\n",
        "my_df = my_df.loc[~too_short_mask][[\n",
        "  's_author_trim', 's_title_trim', 's_content_trim']]\n",
        "print(\"my_df size\", len(my_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gqFpturqM3G",
        "outputId": "dce38335-84ca-4202-de4d-0c8a05e7eb49"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_df size 5513\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "result_dict = {\n",
        "    's_author_trim': [],\n",
        "    's_title_trim': [],\n",
        "    's_content_trim': [],\n",
        "}\n",
        "for i, row in my_df.iterrows():\n",
        "  c = row['s_content_trim']\n",
        "  snippets = list(re.split('，|。|？', c))\n",
        "  lens = [len(s) for s in snippets if s.strip() != '']\n",
        "  if max(lens) != min(lens) or max(lens) not in [5, 7]:\n",
        "    continue\n",
        "  result_dict['s_author_trim'].append(row['s_author_trim'])\n",
        "  result_dict['s_title_trim'].append(row['s_title_trim'])\n",
        "  result_dict['s_content_trim'].append(c)\n",
        "# print(\"get rid of \", sum(bad_items))\n",
        "my_df = pd.DataFrame(data=result_dict)\n",
        "print(\"left\", len(my_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7xjdQCh0qO7J",
        "outputId": "532e635b-edc2-4dd5-f8a5-400dc67e2a79"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "left 4173\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_df.sample(100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "y2PeZMPrqR6l",
        "outputId": "3acd4c70-f359-45c9-e236-afed16b3ead5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     s_author_trim s_title_trim                    s_content_trim\n",
              "2042           张九龄       感遇十二首十          我有异乡忆，宛在云溶溶。凭此目不觏，要之心所钟。\n",
              "1099            李白   杂曲歌辞行路难三首三  有耳莫洗颍川水，有口莫食首阳蕨。含光混世贵无名，何用孤高比云月。\n",
              "1496            张说     杂曲歌辞苏摩遮三  腊月凝阴积帝台，豪歌击鼓送寒来。油囊取得天河水，将添上寿万年杯。\n",
              "3603            田锡         赠杨临察  昔日长安际会时，客情乡思两依依。金门次第俱升达，云路高低各奋飞。\n",
              "196           则天皇后      唐享昊天乐第四          巍巍睿业广，赫赫圣基隆。菲德承先顾，祯符萃眇躬。\n",
              "...            ...          ...                               ...\n",
              "593            郭元振   相和歌辞王昭君三首三          闻有南河信，传闻杀画师。始知君惠重，更遣画蛾眉。\n",
              "2107           释延寿       山居诗其二○  自甘疏拙懒经营，大道从来戒满盈。但起贪心迷有限，谁能触目悟无生。\n",
              "3080            宋白      牡丹诗十首其七  风排香阵拂瑶墀，御苑新晴烂漫时。十五素娥羞水色，三千红面洗胭脂。\n",
              "2717            李昉         赠邓洵美  忆昔词场共着鞭，当时鶑谷喜同迁。关河契阔三千里，音信稀疏二十年。\n",
              "148            明皇帝      赐崔日知往潞州          潞国开新府，壶关宠旧林。妙旌循吏德，持悦庶氓心。\n",
              "\n",
              "[100 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f47a91bc-062b-4720-8013-927b49d296d5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>s_author_trim</th>\n",
              "      <th>s_title_trim</th>\n",
              "      <th>s_content_trim</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2042</th>\n",
              "      <td>张九龄</td>\n",
              "      <td>感遇十二首十</td>\n",
              "      <td>我有异乡忆，宛在云溶溶。凭此目不觏，要之心所钟。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1099</th>\n",
              "      <td>李白</td>\n",
              "      <td>杂曲歌辞行路难三首三</td>\n",
              "      <td>有耳莫洗颍川水，有口莫食首阳蕨。含光混世贵无名，何用孤高比云月。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1496</th>\n",
              "      <td>张说</td>\n",
              "      <td>杂曲歌辞苏摩遮三</td>\n",
              "      <td>腊月凝阴积帝台，豪歌击鼓送寒来。油囊取得天河水，将添上寿万年杯。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3603</th>\n",
              "      <td>田锡</td>\n",
              "      <td>赠杨临察</td>\n",
              "      <td>昔日长安际会时，客情乡思两依依。金门次第俱升达，云路高低各奋飞。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>则天皇后</td>\n",
              "      <td>唐享昊天乐第四</td>\n",
              "      <td>巍巍睿业广，赫赫圣基隆。菲德承先顾，祯符萃眇躬。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>郭元振</td>\n",
              "      <td>相和歌辞王昭君三首三</td>\n",
              "      <td>闻有南河信，传闻杀画师。始知君惠重，更遣画蛾眉。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2107</th>\n",
              "      <td>释延寿</td>\n",
              "      <td>山居诗其二○</td>\n",
              "      <td>自甘疏拙懒经营，大道从来戒满盈。但起贪心迷有限，谁能触目悟无生。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3080</th>\n",
              "      <td>宋白</td>\n",
              "      <td>牡丹诗十首其七</td>\n",
              "      <td>风排香阵拂瑶墀，御苑新晴烂漫时。十五素娥羞水色，三千红面洗胭脂。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2717</th>\n",
              "      <td>李昉</td>\n",
              "      <td>赠邓洵美</td>\n",
              "      <td>忆昔词场共着鞭，当时鶑谷喜同迁。关河契阔三千里，音信稀疏二十年。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>明皇帝</td>\n",
              "      <td>赐崔日知往潞州</td>\n",
              "      <td>潞国开新府，壶关宠旧林。妙旌循吏德，持悦庶氓心。</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f47a91bc-062b-4720-8013-927b49d296d5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f47a91bc-062b-4720-8013-927b49d296d5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f47a91bc-062b-4720-8013-927b49d296d5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b469b235-d8bf-4cd0-a855-4d95771b191b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b469b235-d8bf-4cd0-a855-4d95771b191b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b469b235-d8bf-4cd0-a855-4d95771b191b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 一直以来都是一首诗用两种方式（有/无作者）混合训练，是否最佳？"
      ],
      "metadata": {
        "id": "WXfaaK5PxyVo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "AUTHOR_PROMPT = \"模仿：\"\n",
        "TITLE_PROMPT = \"作诗：\"\n",
        "EOS_TOKEN = ''\n",
        "def build_dataset_df(df, include_author=True):\n",
        "  dfc = df.copy()\n",
        "  if include_author:\n",
        "    dfc['source_text'] = TITLE_PROMPT + df['s_title_trim'] + EOS_TOKEN + AUTHOR_PROMPT + df['s_author_trim']\n",
        "  else:\n",
        "    dfc['source_text'] = TITLE_PROMPT + df['s_title_trim']\n",
        "  dfc['target_text'] = df['s_content_trim']\n",
        "  dfc = dfc[['source_text', 'target_text']]\n",
        "  return dfc\n",
        ""
      ],
      "metadata": {
        "id": "6ZVe2kgnqV-7"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_author_title_content = build_dataset_df(my_df, True)\n",
        "df_author_title_content[100:105]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "Hl5XQSzyqXjN",
        "outputId": "1c1bf2a1-b07a-43af-8e90-9fda911afca2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                source_text                       target_text\n",
              "100            作诗：守岁模仿：高宗皇帝          今宵冬律尽，来朝丽景新。花余凝地雪，条含煖吹分。\n",
              "101  作诗：九月九日幸临渭亭登高得秋模仿：中宗皇帝          九月正乘秋，三杯兴已周。泛桂迎尊满，吹花向酒浮。\n",
              "102       作诗：登骊山高顶寓目模仿：中宗皇帝          四郊秦汉国，八水帝王都。阊阖雄里闬，城阙壮规模。\n",
              "103         作诗：幸秦始皇陵模仿：中宗皇帝          眷言君失德，骊邑想秦余。政烦方改篆，愚俗乃焚书。\n",
              "104       作诗：立春日游苑迎春模仿：中宗皇帝  神皐福地三秦邑，玉台金阙九仙家。寒光犹恋甘泉树，淑景偏临建始花。"
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              "      <th>100</th>\n",
              "      <td>作诗：守岁模仿：高宗皇帝</td>\n",
              "      <td>今宵冬律尽，来朝丽景新。花余凝地雪，条含煖吹分。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>作诗：九月九日幸临渭亭登高得秋模仿：中宗皇帝</td>\n",
              "      <td>九月正乘秋，三杯兴已周。泛桂迎尊满，吹花向酒浮。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>作诗：登骊山高顶寓目模仿：中宗皇帝</td>\n",
              "      <td>四郊秦汉国，八水帝王都。阊阖雄里闬，城阙壮规模。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>作诗：幸秦始皇陵模仿：中宗皇帝</td>\n",
              "      <td>眷言君失德，骊邑想秦余。政烦方改篆，愚俗乃焚书。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>作诗：立春日游苑迎春模仿：中宗皇帝</td>\n",
              "      <td>神皐福地三秦邑，玉台金阙九仙家。寒光犹恋甘泉树，淑景偏临建始花。</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7ff76a55-285a-4f5a-a0de-dd2326919e97 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7ff76a55-285a-4f5a-a0de-dd2326919e97');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c7b7bba0-4e28-4e2a-bf46-8639aad3f8bd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c7b7bba0-4e28-4e2a-bf46-8639aad3f8bd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c7b7bba0-4e28-4e2a-bf46-8639aad3f8bd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_title_content = build_dataset_df(my_df, False)\n",
        "df_title_content[100:105]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "t_4sR_duqbCk",
        "outputId": "0f2cdefe-0372-431c-83eb-89d97393e6b2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         source_text                       target_text\n",
              "100            作诗：守岁          今宵冬律尽，来朝丽景新。花余凝地雪，条含煖吹分。\n",
              "101  作诗：九月九日幸临渭亭登高得秋          九月正乘秋，三杯兴已周。泛桂迎尊满，吹花向酒浮。\n",
              "102       作诗：登骊山高顶寓目          四郊秦汉国，八水帝王都。阊阖雄里闬，城阙壮规模。\n",
              "103         作诗：幸秦始皇陵          眷言君失德，骊邑想秦余。政烦方改篆，愚俗乃焚书。\n",
              "104       作诗：立春日游苑迎春  神皐福地三秦邑，玉台金阙九仙家。寒光犹恋甘泉树，淑景偏临建始花。"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4a9002a1-3092-4b46-903d-6a8284778a21\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>source_text</th>\n",
              "      <th>target_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>作诗：守岁</td>\n",
              "      <td>今宵冬律尽，来朝丽景新。花余凝地雪，条含煖吹分。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>作诗：九月九日幸临渭亭登高得秋</td>\n",
              "      <td>九月正乘秋，三杯兴已周。泛桂迎尊满，吹花向酒浮。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>作诗：登骊山高顶寓目</td>\n",
              "      <td>四郊秦汉国，八水帝王都。阊阖雄里闬，城阙壮规模。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>作诗：幸秦始皇陵</td>\n",
              "      <td>眷言君失德，骊邑想秦余。政烦方改篆，愚俗乃焚书。</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>作诗：立春日游苑迎春</td>\n",
              "      <td>神皐福地三秦邑，玉台金阙九仙家。寒光犹恋甘泉树，淑景偏临建始花。</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a9002a1-3092-4b46-903d-6a8284778a21')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4a9002a1-3092-4b46-903d-6a8284778a21 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4a9002a1-3092-4b46-903d-6a8284778a21');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-62bc3fdb-3452-478a-bee5-8a9f8601a004\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62bc3fdb-3452-478a-bee5-8a9f8601a004')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-62bc3fdb-3452-478a-bee5-8a9f8601a004 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 转化为Trainer喜欢的数据格式"
      ],
      "metadata": {
        "id": "BVa53wGnx_2-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Copied from Alpaca-LoRA, notice input_ids, attention_mask, and labels are\n",
        "# default expected columns in huggingface dataset lib\n",
        "def tokenize(tokenizer, prompt, cutoff_len, add_eos_token=True):\n",
        "  # there's probably a way to do this with the tokenizer settings\n",
        "  # but again, gotta move fast\n",
        "  result = tokenizer(\n",
        "      prompt,\n",
        "      truncation=True,\n",
        "      max_length=cutoff_len,\n",
        "      padding=False,\n",
        "      return_tensors=None,\n",
        "  )\n",
        "  if (\n",
        "      result[\"input_ids\"][-1] != tokenizer.eos_token_id\n",
        "      and len(result[\"input_ids\"]) < cutoff_len\n",
        "      and add_eos_token\n",
        "  ):\n",
        "    result[\"input_ids\"].append(tokenizer.eos_token_id)\n",
        "    result[\"attention_mask\"].append(1)\n",
        "\n",
        "  # result[\"labels\"] = copy.deepcopy(result[\"input_ids\"])\n",
        "  result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "  return result\n",
        "\n",
        "\n",
        "# Max num of tokens (including prompt and output), chinese encoding takes more\n",
        "# than # of chars as observed\n",
        "CUTOFF_LEN = 96\n",
        "# Predict training prompt as well to increase quality as Alpaca Lora does.\n",
        "# Turn off to speedup, but might affect quality.\n",
        "TRAIN_ON_INPUT = False\n",
        "\n",
        "# Branched from Alpaca-LoRA\n",
        "def tokenize_fn(data_point):\n",
        "  prompt_in, prompt_out = data_point['source_text'], data_point['target_text']\n",
        "  full_prompt = prompt_in + prompt_out\n",
        "  tokenized_full_prompt = tokenize(tokenizer, full_prompt, CUTOFF_LEN)\n",
        "  if not TRAIN_ON_INPUT:\n",
        "    user_prompt = prompt_in\n",
        "    tokenized_user_prompt = tokenize(tokenizer, user_prompt, CUTOFF_LEN, add_eos_token=False)\n",
        "    user_prompt_len = len(tokenized_user_prompt[\"input_ids\"])\n",
        "    tokenized_full_prompt[\"labels\"] = [\n",
        "        -100 # special id for skipping\n",
        "    ] * user_prompt_len + tokenized_full_prompt[\"labels\"][user_prompt_len:]\n",
        "  return tokenized_full_prompt\n",
        "\n",
        "\n",
        "train_ds = Dataset.from_pandas(df_title_content)\n",
        "train_ds = train_ds.flatten()\n",
        "tokenized_train_ds = train_ds.map(\n",
        "    tokenize_fn,\n",
        "    remove_columns=['source_text', 'target_text'],\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "013d6e7031084ff294891e296ea1eb44",
            "907bfca5a72d4623b47dda44f815e113",
            "e50d7c3d7438439ebb3898376f1730d3",
            "04b1dde056ee44dfb0e4093577e7a1c0",
            "7005016dbe694601890479aed70b3b64",
            "8bd2583ce18444daac7ab3c79b17807b",
            "bbb08cbe639847f89f0d83b842fe805d",
            "c32a1e58438544999afb6f7adb95543c",
            "ced85d332d5e4d499b403647b7641118",
            "f5481ea03c9a4d3f8d5f093f2bbf10eb",
            "522613a1f78a4285abf5a8626707f8cb"
          ]
        },
        "id": "EinzgSLmqwgd",
        "outputId": "3fc873c7-9726-444e-b9fc-1e2f9d3ed810"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4173 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "013d6e7031084ff294891e296ea1eb44"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Optionally check a few examples by decoding the inputs\n",
        "for i in range(100, 103):\n",
        "  print(\"token length\", len(tokenized_train_ds['input_ids'][i]))\n",
        "  print(tokenizer.decode(tokenized_train_ds['input_ids'][i]))\n",
        "  print(\"Label ids\", tokenized_train_ds['labels'][i])\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cscsVirWrJqm",
        "outputId": "a0a045fb-27cc-4462-9367-4c2bb1c0d173"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "token length 31\n",
            " 作诗：守岁今宵冬律尽，来朝丽景新。花余凝地雪，条含煖吹分。</s>\n",
            "Label ids [-100, -100, -100, -100, -100, -100, 31541, 33783, 32324, 31859, 31884, 72, 31192, 32283, 32126, 31768, 31226, 73, 31528, 32012, 32837, 31218, 32269, 72, 31594, 31939, 41715, 32886, 31221, 73, 2]\n",
            "\n",
            "token length 41\n",
            " 作诗：九月九日幸临渭亭登高得秋九月正乘秋，三杯兴已周。泛桂迎尊满，吹花向酒浮。</s>\n",
            "Label ids [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 32110, 31245, 31383, 32329, 32208, 72, 31318, 32576, 31906, 31453, 31599, 73, 32830, 32780, 32068, 32518, 31672, 72, 32886, 31528, 31490, 31805, 32845, 73, 2]\n",
            "\n",
            "token length 35\n",
            " 作诗：登骊山高顶寓目四郊秦汉国，八水帝王都。阊阖雄里闬，城阙壮规模。</s>\n",
            "Label ids [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 31533, 33826, 32884, 31976, 31200, 72, 31978, 31323, 32577, 31702, 31228, 73, 38289, 35402, 32409, 31304, 47737, 72, 31454, 35270, 32849, 7517, 73, 2]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 设置LoRA"
      ],
      "metadata": {
        "id": "cl6tsedEyFbB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "original_8bit_llama_model = model"
      ],
      "metadata": {
        "id": "0eFpF0hKraUE"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = prepare_model_for_int8_training(original_8bit_llama_model)\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32, # scaling param related to r, reuse alpaca-lora\n",
        "    # only W-pack works https://github.com/beyondguo/LLM-Tuning/blob/master/baichuan_lora_tuning.py\n",
        "    target_modules=['W_pack'],  # [\"q_proj\", \"v_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "lora_model = get_peft_model(original_8bit_llama_model, config)"
      ],
      "metadata": {
        "id": "qfIzy6V6rQIK"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out meaning of the Chinese char using ChatGPT\n",
        "def eval_model(my_model, examples=[\"标题： 作诗：秋思\\n诗歌： \",\n",
        "                                   \"标题： 作诗：佳人有约\\n诗歌： \",\n",
        "                                   \"标题： 作诗：冬雪 模仿：杜甫\\n诗歌： \",\n",
        "                                   \"标题： 作诗：冬雪 模仿：苏轼\\n诗歌： \"]):\n",
        "  for p_in in examples:\n",
        "    inputs = tokenizer(p_in, return_tensors='pt')\n",
        "    inputs = inputs.to('cuda:0')\n",
        "    pred = my_model.generate(**inputs, max_new_tokens=64,repetition_penalty=1.1)\n",
        "    print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "id": "3-SmtD8IrsOi"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_model(lora_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6RdE2ie8sPdD",
        "outputId": "26a9a10f-39e2-40a7-afe2-ecd6452e6484"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "标题： 作诗：秋思\n",
            "诗歌： 《秋思》作者:李文涛(一)落叶飘零，寒风萧瑟。天高云淡，雁去无声。菊花绽放，枫红似火。月光如水，思念成河。 (二)夜深人静，孤灯独坐。举\n",
            "标题： 佳人有约：秋思\n",
            "诗歌： 《秋思》作者:李建国(一)落叶飘零，寒风萧瑟。天高云淡，雁去无声。菊花绽放，枫红似火。月光如水，夜色朦胧。思念故乡，情深意浓。 (二)窗\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 这里所有参数都要重新考虑， 特别以下\n",
        "- per_device_train_batch_size\n",
        "- gradient_accumulation_steps\n",
        "- learning_rate"
      ],
      "metadata": {
        "id": "hTjwpEP3yIdf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# As you can tell, I even omitted eval_dataset for this demo :(\n",
        "trainer = transformers.Trainer(\n",
        "    model=lora_model,\n",
        "    train_dataset=tokenized_train_ds,\n",
        "    args=transformers.TrainingArguments(\n",
        "        # increased batch size will significantly increase GPU requirement here\n",
        "        # Decrease to 4 if you have less than 16G vram\n",
        "        # Batch = 4, probably 8.3-8.8G vram\n",
        "        # Batch = 16, 9.5G+\n",
        "        # Batch = 32, 11G+\n",
        "        # Batch = 64, 14G+\n",
        "        per_device_train_batch_size=32,\n",
        "        gradient_accumulation_steps=2,\n",
        "        warmup_steps=8,\n",
        "        num_train_epochs=2,\n",
        "        learning_rate=2e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=20,\n",
        "        output_dir='outputs',\n",
        "        remove_unused_columns=False,\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForSeq2Seq(\n",
        "        tokenizer, pad_to_multiple_of=8, return_tensors=\"pt\", padding=True,\n",
        "    ),\n",
        ")\n",
        "lora_model.config"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzxHzGhpsZJX",
        "outputId": "ed1de575-7510-4b46-a062-d5bfddf00d07"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BaiChuanConfig {\n",
              "  \"_name_or_path\": \"baichuan-inc/Baichuan-7B\",\n",
              "  \"architectures\": [\n",
              "    \"BaiChuanForCausalLM\"\n",
              "  ],\n",
              "  \"auto_map\": {\n",
              "    \"AutoConfig\": \"baichuan-inc/Baichuan-7B--configuration_baichuan.BaiChuanConfig\",\n",
              "    \"AutoModelForCausalLM\": \"baichuan-inc/Baichuan-7B--modeling_baichuan.BaiChuanForCausalLM\"\n",
              "  },\n",
              "  \"bos_token_id\": 1,\n",
              "  \"eos_token_id\": 2,\n",
              "  \"hidden_act\": \"silu\",\n",
              "  \"hidden_size\": 4096,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"intermediate_size\": 11008,\n",
              "  \"max_position_embeddings\": 4096,\n",
              "  \"model_type\": \"baichuan\",\n",
              "  \"num_attention_heads\": 32,\n",
              "  \"num_hidden_layers\": 32,\n",
              "  \"pad_token_id\": 0,\n",
              "  \"quantization_config\": {\n",
              "    \"bnb_4bit_compute_dtype\": \"float32\",\n",
              "    \"bnb_4bit_quant_type\": \"fp4\",\n",
              "    \"bnb_4bit_use_double_quant\": false,\n",
              "    \"llm_int8_enable_fp32_cpu_offload\": false,\n",
              "    \"llm_int8_has_fp16_weight\": false,\n",
              "    \"llm_int8_skip_modules\": null,\n",
              "    \"llm_int8_threshold\": 6.0,\n",
              "    \"load_in_4bit\": false,\n",
              "    \"load_in_8bit\": true,\n",
              "    \"quant_method\": \"bitsandbytes\"\n",
              "  },\n",
              "  \"rms_norm_eps\": 1e-06,\n",
              "  \"tie_word_embeddings\": false,\n",
              "  \"torch_dtype\": \"float32\",\n",
              "  \"transformers_version\": \"4.33.2\",\n",
              "  \"use_cache\": true,\n",
              "  \"vocab_size\": 64000\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 开炼！"
      ],
      "metadata": {
        "id": "2xzPo0qnyWJA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "_XMazmEZsq-H",
        "outputId": "1dcd9439-a395-4d95-ef72-1d6273e79215"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers_modules.baichuan-inc.Baichuan-7B.c1a5c7d5b7f50ecc51bb0e08150a9f12e5656756.modeling_baichuan:`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='130' max='130' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [130/130 08:29, Epoch 1/2]\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>3.989500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>3.684300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>3.572700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>3.547200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>3.510200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>3.538400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=130, training_loss=3.6314940525935246, metrics={'train_runtime': 514.7163, 'train_samples_per_second': 16.215, 'train_steps_per_second': 0.253, 'total_flos': 1.6803104830390272e+16, 'train_loss': 3.6314940525935246, 'epoch': 1.98})"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 测试炼后结果"
      ],
      "metadata": {
        "id": "fxP5AODsyaFN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval_model(lora_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpSgXsDRsvQj",
        "outputId": "6854de48-dcae-4e67-96f2-28e41bcd16ec"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "标题： 作诗：秋思\n",
            "诗歌： 寒蝉凄切，对长亭晚，骤雨初歇。都门帐饮无绪，留恋处、兰舟催发。执手相看泪眼，竟无语凝噎。念去去千里烟波，暮霭沉沉楚天阔。\n",
            "标题： 作诗：佳人有约\n",
            "诗歌： 红妆翠袖，玉貌花容。风流自是天上种，不比人间富贵荣华。\n",
            "标题： 作诗：冬雪 模仿：杜甫\n",
            "诗歌： 严陵滩上白蘋风，江北江南雪正浓。\n",
            "标题： 作诗：冬雪 模仿：苏轼\n",
            "诗歌： 梅花已报春消息，红蕊繁枝满院香。\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TODO：上传LoRA到HF"
      ],
      "metadata": {
        "id": "Zc8FnRbdydx_"
      }
    }
  ]
}