finetune_prompttune_demo_on_collab_v1.00.ipynb

finetune_prompttune_demo_on_collab_v1-00.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/tae0y/7bcda9a40ec6c50187d8019e728dbdec/finetune_prompttune_demo_on_collab_v1-00.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://huggingface.co/blog/gemma-peft"
      ],
      "metadata": {
        "id": "nUqkImzqXKU-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "T4, python3.10"
      ],
      "metadata": {
        "id": "yq-60owHaUHG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LoRA(Low-Rank Adaptation)** : 대규모 언어 모델을 위한 파라미터 효율적인 미세 조정 기술 중 하나.  \n",
        "미세 조정할 총 모델 매개 변수 수의 일부만 처리하며, 원래 모델을 고정하고 낮은 순위의 행렬로 분해되는 어댑터 계층만 학습."
      ],
      "metadata": {
        "id": "rNJ27NSfX8ji"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install accelerate bitsandbytes peft torch transformers datasets trl"
      ],
      "metadata": {
        "id": "yoJ3_GueebFR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jpYhi2H-Wbuv"
      },
      "outputs": [],
      "source": [
        "from peft import LoraConfig\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=8,\n",
        "    target_modules=[\"q_proj\", \"o_proj\", \"k_proj\", \"v_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://huggingface.co/google/gemma-7b-it  \n",
        "위 경로에서 gemma 모델 사용 라이센스에 동의하고   \n",
        "huggingface에서 액세스토큰을 발급해 넣는다  "
      ],
      "metadata": {
        "id": "WU65iKi1Y5oa"
      }
    },
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        "HF_TOKEN = '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'"
      ],
      "metadata": {
        "id": "SufaGArTaKaz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "model_id = \"google/gemma-2b\"\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0}, token=HF_TOKEN)"
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      },
      "execution_count": null,
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        {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Quote: Imagination is more\"\n",
        "device = \"cuda:0\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens=100)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "arjplloedXmx",
        "outputId": "25c32480-8307-4a0a-9f92-487ae237aee1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quote: Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world.\n",
            "\n",
            "- Albert Einstein\n",
            "\n",
            "The following is a list of the most popular and most recent quotes from Albert Einstein.\n",
            "\n",
            "<h2>Albert Einstein Quotes</h2>\n",
            "\n",
            "1. “Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world.”\n",
            "\n",
            "2. “Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world.”\n",
            "\n",
            "3. “Imagination is more important than knowledge. Knowledge is\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Quote: Imagination is more\\nAuthor: \"\n",
        "device = \"cuda:0\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens=100)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpUyevZngBKL",
        "outputId": "463b7b6a-5109-4450-c686-a5789b78f351"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quote: Imagination is more\n",
            "Author: <strong><em>Dr. Seuss</em></strong>\n",
            "\n",
            "<strong><em>“Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world, stimulating progress, inspiring creativity, and beyond.”</em></strong>\n",
            "\n",
            "<strong><em>Dr. Seuss</em></strong>\n",
            "\n",
            "<strong><em>“Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world, stimulating progress, inspiring creativity, and beyond.”</em></strong>\n",
            "\n",
            "<strong><em>Dr. Seuss</em></strong>\n",
            "\n",
            "<strong><em>“Imagination is\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "위와 같이 미세조정되지 않은 모델은, 텍스트를 중구난방으로 생성한다.  \n",
        "원하는 형식으로 출력하기 위해 모델을 학습시켜보겠다."
      ],
      "metadata": {
        "id": "HBaJGfPodvoP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "data = load_dataset(\"Abirate/english_quotes\") #영어 인용 데이터셋\n",
        "data = data.map(lambda samples: tokenizer(samples[\"quote\"]), batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274,
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        },
        "id": "vFCFDBJUdkIT",
        "outputId": "d3293b55-5605-4ad3-90c9-f72d75d6d2d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/5.55k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0e98254b2894cadb7cc798468f59839"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/647k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20d6ed907dbd41dfa71ff1734875479b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab7e5b338d4244e9ac590ec68b8be983"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from trl import SFTTrainer\n",
        "\n",
        "def formatting_func(example):\n",
        "    output_texts = []\n",
        "    for i in range(len(example)): #질의응답 형식을 조정\n",
        "        text = f\"Quote: {example['quote'][i]}\\nAuthor: {example['author'][i]}\"\n",
        "        output_texts.append(text)\n",
        "    return output_texts\n",
        "\n",
        "model4tune = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0}, token=HF_TOKEN)\n",
        "trainer = SFTTrainer(\n",
        "    model=model4tune,\n",
        "    train_dataset=data[\"train\"],\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=2,\n",
        "        max_steps=10,\n",
        "        learning_rate=2e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir=\"outputs\",\n",
        "        optim=\"paged_adamw_8bit\"\n",
        "    ),\n",
        "    peft_config=lora_config,\n",
        "    formatting_func=formatting_func,\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654,
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            "4d16beebbdc24a2ca19b9cae60a80853",
            "3a61947495634ba7b1b33b60658b25ad"
          ]
        },
        "id": "3ei6bgZ6d9p6",
        "outputId": "ecc3ea8c-a9e4-4337-ea2a-c0e5ab305e7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f38ad69aa7a64df89b8d035378c19d08"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:225: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3ac0631bcc0a42f79e31fd9c08a6e793"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:294: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10/10 00:11, Epoch 2/4]\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>1</td>\n",
              "      <td>2.449900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.293200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.023000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.377300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.757800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.789400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.186000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.263900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.531400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.668000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10, training_loss=2.0339842319488524, metrics={'train_runtime': 13.1816, 'train_samples_per_second': 3.035, 'train_steps_per_second': 0.759, 'total_flos': 35910175027200.0, 'train_loss': 2.0339842319488524, 'epoch': 2.67})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Quote: Imagination is\"\n",
        "device = \"cuda:0\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "\n",
        "outputs = model4tune.generate(**inputs, max_new_tokens=100)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kIZXwvJbfpB6",
        "outputId": "ed540bb6-dcf7-436e-9546-263a7d8e066a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quote: Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world.\n",
            "\n",
            "Author: Albert Einstein\n",
            "\n",
            "<h2><strong>What is Imagination?</strong></h2>\n",
            "\n",
            "Imagination is the ability to create and imagine things. It is the ability to think of things that are not real. It is the ability to think of things that are not real. It is the ability to think of things that are not real.\n",
            "\n",
            "Imagination is the ability to think of things that are not real. It is the ability to\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Quote: Imagination is more\\nAuthor: \"\n",
        "device = \"cuda:0\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "\n",
        "outputs = model4tune.generate(**inputs, max_new_tokens=100)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVDWl9YAgH4m",
        "outputId": "4f127441-7dd3-46f8-e88b-c7e69a618732"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quote: Imagination is more\n",
            "Author: <strong><em>Dr. Seuss</em></strong>\n",
            "\n",
            "<h2><strong><em><u>Explanation:</u></em></strong></h2>\n",
            "\n",
            "<strong><em><u>The quote is from Dr. Seuss.</u></em></strong>\n",
            "\n",
            "<strong><em><u>The quote is about imagination.</u></em></strong>\n",
            "\n",
            "<strong><em><u>The quote is about the power of imagination.</u></em></strong>\n",
            "\n",
            "<strong><em><u>The quote is about the power of imagination.</u></em></strong>\n",
            "\n",
            "<strong><em><u>The quote is about the power of imagination.</u></em></strong>\n",
            "\n",
            "<strong><em><u>The quote\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TPU에서 가속 학습 샘플코드 ~\n",
        "\n",
        "```python\n",
        "from transformers import DataCollatorForLanguageModeling, Trainer, TrainingArguments\n",
        "\n",
        "# Set up the FSDP config. To enable FSDP via SPMD, set xla_fsdp_v2 to True.\n",
        "fsdp_config = {\n",
        "    \"fsdp_transformer_layer_cls_to_wrap\": [\"GemmaDecoderLayer\"],\n",
        "    \"xla\": True,\n",
        "    \"xla_fsdp_v2\": True,\n",
        "    \"xla_fsdp_grad_ckpt\": True\n",
        "}\n",
        "\n",
        "# Finally, set up the trainer and train the model.\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    train_dataset=data,\n",
        "    args=TrainingArguments(\n",
        "        per_device_train_batch_size=64,  # This is actually the global batch size for SPMD.\n",
        "        num_train_epochs=100,\n",
        "        max_steps=-1,\n",
        "        output_dir=\"./output\",\n",
        "        optim=\"adafactor\",\n",
        "        logging_steps=1,\n",
        "        dataloader_drop_last = True,  # Required for SPMD.\n",
        "        fsdp=\"full_shard\",\n",
        "        fsdp_config=fsdp_config,\n",
        "    ),\n",
        "    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "trainer.train()\n",
        "\n",
        "```"
      ],
      "metadata": {
        "id": "tWWxHrV7fyrE"
      }
    }
  ]
}