{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "lw1cWgq-DI5k",
      "metadata": {
        "id": "lw1cWgq-DI5k"
      },
      "source": [
        "# Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "kBFPA3-aDT7H",
      "metadata": {
        "id": "kBFPA3-aDT7H"
      },
      "source": [
        "In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!\n",
        "\n",
        "We will finetune the model on [`financial_phrasebank`](https://huggingface.co./datasets/financial_phrasebank) dataset, that consists of pairs of text-labels to classify financial-related sentences, if they are either `positive`, `neutral` or `negative`.\n",
        "\n",
        "Note that you could use the same notebook to fine-tune `flan-t5-xl` as well, but you would need to shard the models first to avoid CPU RAM issues on Google Colab, check [these weights](https://huggingface.co./ybelkada/flan-t5-xl-sharded-bf16)."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TODO #1\n",
        "\n",
        "`google/flan-t5-large` 모델은 무엇을 목표로 만들어졌고 기대할 수 있는 기능은 무엇인지 조사하시오\n",
        "- 마크다운 스타일로 작성하시오"
      ],
      "metadata": {
        "id": "5TXx1vj8kJSu"
      },
      "id": "5TXx1vj8kJSu"
    },
    {
      "cell_type": "markdown",
      "id": "ShAuuHCDDkvk",
      "metadata": {
        "id": "ShAuuHCDDkvk"
      },
      "source": [
        "## Install requirements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "DRQ4ZrJTDkSy",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DRQ4ZrJTDkSy",
        "outputId": "1c5f7aef-88f6-44b7-8fdc-a8ba40e3fa71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.6/519.6 kB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m258.1/258.1 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.8/294.8 kB\u001b[0m \u001b[31m30.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m42.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m59.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q bitsandbytes datasets accelerate\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QBdCIrizDxFw",
      "metadata": {
        "id": "QBdCIrizDxFw"
      },
      "source": [
        "## Import model and tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "dd3c5acc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
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          ]
        },
        "id": "dd3c5acc",
        "outputId": "b0ff6b65-6ceb-4076-b98a-f875dd1b65e7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/662 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "442d1712c543457cb159ef5b71a83643"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/3.13G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0086ace4933847ee963b144470c38a22"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d92807869074789800be8b4e1767935"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84e07a50eabd429cbdab8d8b4607d1f9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "58132995934344e2b54dbf4a5ee88e6f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "973e6b2f0bbd4bcbb68d14c149d1929d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9b6f47ec72cb4b77a393cd187366fbfd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Select CUDA device index\n",
        "import os\n",
        "import torch\n",
        "\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
        "\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "\n",
        "model_name = \"google/flan-t5-large\"\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_name, load_in_8bit=True)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "VwcHieQzD_dl",
      "metadata": {
        "id": "VwcHieQzD_dl"
      },
      "source": [
        "## Prepare model for training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4o3ePxrjEDzv",
      "metadata": {
        "id": "4o3ePxrjEDzv"
      },
      "source": [
        "Some pre-processing needs to be done before training such an int8 model using `peft`, therefore let's import an utiliy function `prepare_model_for_int8_training` that will:\n",
        "- Casts all the non `int8` modules to full precision (`fp32`) for stability\n",
        "- Add a `forward_hook` to the input embedding layer to enable gradient computation of the input hidden states\n",
        "- Enable gradient checkpointing for more memory-efficient training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "1629ebcb",
      "metadata": {
        "id": "1629ebcb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bb505d4-b55e-4ce4-8c64-5c98a26bd483"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:133: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from peft import prepare_model_for_int8_training\n",
        "\n",
        "model = prepare_model_for_int8_training(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "iCpAgawAEieu",
      "metadata": {
        "id": "iCpAgawAEieu"
      },
      "source": [
        "## Load your `PeftModel`\n",
        "\n",
        "Here we will use LoRA (Low-Rank Adaptators) to train our model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "17566ae3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17566ae3",
        "outputId": "4f76af17-b108-4b0f-bbe7-cfb3b5db9cae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 4718592 || all params: 787868672 || trainable%: 0.5989059049678777\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, get_peft_model, TaskType\n",
        "\n",
        "\n",
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )\n",
        "\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=16, lora_alpha=32, target_modules=[\"q\", \"v\"], lora_dropout=0.05, bias=\"none\", task_type=\"SEQ_2_SEQ_LM\"\n",
        ")\n",
        "\n",
        "\n",
        "model = get_peft_model(model, lora_config)\n",
        "print_trainable_parameters(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "mGkwIgNXyS7U",
      "metadata": {
        "id": "mGkwIgNXyS7U"
      },
      "source": [
        "As you can see, here we are only training 0.6% of the parameters of the model! This is a huge memory gain that will enable us to fine-tune the model without any memory issue."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TODO #2\n",
        "\n",
        "위와 같이 0.6%로 학습 파라미터의 갯수가 대폭 축소된 원리에 대해 개략적으로 조사하시오.\n",
        "- 마크다운 스타일로 작성하시오"
      ],
      "metadata": {
        "id": "9kkyrzsakn2b"
      },
      "id": "9kkyrzsakn2b"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TODO #3\n",
        "\n",
        "모델 로드시 `load_in_8bit=True` 옵션을 사용하지 않으면 원본을 로딩한다.\n",
        "\n",
        "이 때의 모델 구조와,  `load_in_8bit=True` 을 사용했을 때의 무델 구조를 비교하여 어떤 차이점이 있는지를 조사하시오.\n",
        "- 마크다운 스타일로 작성하시오"
      ],
      "metadata": {
        "id": "wgvqtHnFlNAl"
      },
      "id": "wgvqtHnFlNAl"
    },
    {
      "cell_type": "markdown",
      "id": "HsG0x6Z7FwjZ",
      "metadata": {
        "id": "HsG0x6Z7FwjZ"
      },
      "source": [
        "## Load and process data\n",
        "\n",
        "Here we will use [`financial_phrasebank`](https://huggingface.co./datasets/financial_phrasebank) dataset to fine-tune our model on sentiment classification on financial sentences. We will load the split `sentences_allagree`, which corresponds according to the model card to the split where there is a 100% annotator agreement."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "242cdfae",
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.04k [00:00<?, ?B/s]"
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          "output_type": "display_data",
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/8.88k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/2264 [00:00<?, ? examples/s]"
            ],
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              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "Map:   0%|          | 0/2037 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e5876ca2b5a431eb316d131443d1f3e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Map:   0%|          | 0/227 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "5bb08353681743748087ac241819d97c"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# loading dataset\n",
        "dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\n",
        "dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
        "dataset[\"validation\"] = dataset[\"test\"]\n",
        "del dataset[\"test\"]\n",
        "\n",
        "classes = dataset[\"train\"].features[\"label\"].names\n",
        "dataset = dataset.map(\n",
        "    lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
        "    batched=True,\n",
        "    num_proc=1,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "qzwyi-Z9yzRF",
      "metadata": {
        "id": "qzwyi-Z9yzRF"
      },
      "source": [
        "Let's also apply some pre-processing of the input data, the labels needs to be pre-processed, the tokens corresponding to `pad_token_id` needs to be set to `-100` so that the `CrossEntropy` loss associated with the model will correctly ignore these tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "6b7ea44c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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        },
        "id": "6b7ea44c",
        "outputId": "7c4ef1c5-e97c-4807-e8b3-b38f0072f3f5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running tokenizer on dataset:   0%|          | 0/2037 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21ee99c78e8f4b96b8dbb734a38469e1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running tokenizer on dataset:   0%|          | 0/227 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "621b06b206934c538d508a4ad996c3d4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# data preprocessing\n",
        "text_column = \"sentence\"\n",
        "label_column = \"text_label\"\n",
        "max_length = 128\n",
        "\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = examples[text_column]\n",
        "    targets = examples[label_column]\n",
        "    model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
        "    labels = tokenizer(targets, max_length=3, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
        "    labels = labels[\"input_ids\"]\n",
        "    labels[labels == tokenizer.pad_token_id] = -100\n",
        "    model_inputs[\"labels\"] = labels\n",
        "    return model_inputs\n",
        "\n",
        "\n",
        "processed_datasets = dataset.map(\n",
        "    preprocess_function,\n",
        "    batched=True,\n",
        "    num_proc=1,\n",
        "    remove_columns=dataset[\"train\"].column_names,\n",
        "    load_from_cache_file=False,\n",
        "    desc=\"Running tokenizer on dataset\",\n",
        ")\n",
        "\n",
        "train_dataset = processed_datasets[\"train\"]\n",
        "eval_dataset = processed_datasets[\"validation\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TODO #4\n",
        "\n",
        "위 데이터셋 로딩/가공에서 사용한 허브의 데이터셋 `financial_phrasebank` 구조와 이 셋이 어떻게 미세튜닝에 활용되었는지 개략적으로 조사하시오.\n",
        "- 마크다운 스타일로 작성하시오"
      ],
      "metadata": {
        "id": "zmh21tjCm01z"
      },
      "id": "zmh21tjCm01z"
    },
    {
      "cell_type": "markdown",
      "id": "bcNTdVypGEPb",
      "metadata": {
        "id": "bcNTdVypGEPb"
      },
      "source": [
        "## Train our model!\n",
        "\n",
        "Let's now train our model, run the cells below.\n",
        "Note that for T5 since some layers are kept in `float32` for stability purposes there is no need to call autocast on the trainer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "69c756ac",
      "metadata": {
        "id": "69c756ac"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    \"temp\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=1e-3,\n",
        "    gradient_accumulation_steps=1,\n",
        "    auto_find_batch_size=True,\n",
        "    num_train_epochs=1,\n",
        "    save_steps=100,\n",
        "    save_total_limit=8,\n",
        ")\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "ab52b651",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "ab52b651",
        "outputId": "cbf80da6-cdba-4fa7-f7bc-b039972bd137"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='255' max='255' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [255/255 07:35, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.008438</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=255, training_loss=0.2743978462967218, metrics={'train_runtime': 462.5396, 'train_samples_per_second': 4.404, 'train_steps_per_second': 0.551, 'total_flos': 1181084919791616.0, 'train_loss': 0.2743978462967218, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "r98VtofiGXtO",
      "metadata": {
        "id": "r98VtofiGXtO"
      },
      "source": [
        "## Qualitatively test our model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "NIm7z3UNzGPP",
      "metadata": {
        "id": "NIm7z3UNzGPP"
      },
      "source": [
        "Let's have a quick qualitative evaluation of the model, by taking a sample from the dataset that corresponds to a positive label. Run your generation similarly as you were running your model from `transformers`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "c95d6173",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c95d6173",
        "outputId": "81977768-2fe6-4472-b214-0ba66343c62d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1421: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co./docs/transformers/generation_strategies#default-text-generation-configuration )\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1539: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input sentence:  In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\n",
            " output prediction:  ['positive']\n"
          ]
        }
      ],
      "source": [
        "model.eval()\n",
        "input_text = \"In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\"\n",
        "inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
        "\n",
        "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
        "\n",
        "print(\"input sentence: \", input_text)\n",
        "print(\" output prediction: \", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TODO #5\n",
        "\n",
        "본인의 허깅페이스 계정을 만들고 아래 허브에 업로드/다운로드/확인 과정을 본인 계정 기준으로 진행하시오.\n",
        "\n",
        "진행 후 업르드한 허깅페이스 허브의 모델 id를 적으시오.\n",
        "- 마크다운 스타일로 작성하시오."
      ],
      "metadata": {
        "id": "ubwn2Qdbl3Fb"
      },
      "id": "ubwn2Qdbl3Fb"
    },
    {
      "cell_type": "markdown",
      "id": "9QqBlwzoGZ3f",
      "metadata": {
        "id": "9QqBlwzoGZ3f"
      },
      "source": [
        "## Share your adapters on 🤗 Hub"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "NT-C8SjcKqUx",
      "metadata": {
        "id": "NT-C8SjcKqUx"
      },
      "source": [
        "Once you have trained your adapter, you can easily share it on the Hub using the method `push_to_hub` . Note that only the adapter weights and config will be pushed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bcbfa1f9",
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        "colab": {
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        "id": "bcbfa1f9",
        "outputId": "91ef770e-9fc4-4eb2-b02b-24e635101f97"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
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          "text": [
            "Uploading the following files to ybelkada/flan-t5-large-lora: adapter_model.bin,adapter_config.json\n"
          ]
        },
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              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
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              "CommitInfo(commit_url='https://huggingface.co./ybelkada/flan-t5-large-lora/commit/585e4e8cac66188692d506ac107e2d2884c0a4a0', commit_message='Upload model', commit_description='', oid='585e4e8cac66188692d506ac107e2d2884c0a4a0', pr_url=None, pr_revision=None, pr_num=None)"
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      ],
      "source": [
        "model.push_to_hub(\"ybelkada/flan-t5-large-financial-phrasebank-lora\", use_auth_token=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xHuDmbCYJ89f",
      "metadata": {
        "id": "xHuDmbCYJ89f"
      },
      "source": [
        "## Load your adapter from the Hub"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ANFo6DdfKlU3",
      "metadata": {
        "id": "ANFo6DdfKlU3"
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      "source": [
        "You can load the model together with the adapter with few lines of code! Check the snippet below to load the adapter from the Hub and run the example evaluation!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "j097aaPWJ-9u",
      "metadata": {
        "id": "j097aaPWJ-9u"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "\n",
        "peft_model_id = \"ybelkada/flan-t5-large-financial-phrasebank-lora\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, torch_dtype=\"auto\", device_map=\"auto\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "\n",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ]
    },
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      "execution_count": null,
      "id": "jmjwWYt0KI_I",
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        "id": "jmjwWYt0KI_I",
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      "outputs": [
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Generate config GenerationConfig {\n",
            "  \"_from_model_config\": true,\n",
            "  \"decoder_start_token_id\": 0,\n",
            "  \"eos_token_id\": 1,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"transformers_version\": \"4.27.0.dev0\"\n",
            "}\n",
            "\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1374: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "input sentence:  In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\n",
            " output prediction:  ['positive']\n"
          ]
        }
      ],
      "source": [
        "model.eval()\n",
        "input_text = \"In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\"\n",
        "inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
        "\n",
        "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
        "\n",
        "print(\"input sentence: \", input_text)\n",
        "print(\" output prediction: \", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
      ]
    }
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