15_PEFT_Finetune_Bloom7B_tagger.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/Transformers/blob/main/15_PEFT_Finetune_Bloom7B_tagger.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WE5GJ6s7y0Xo"
      },
      "source": [
        "# Using 🤗 PEFT & bitsandbytes to finetune a LoRa checkpoint\n",
        "\n",
        "\n",
        "COde walkthrough video: https://www.youtube.com/watch?v=Us5ZFp16PaU\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otj46qRbtpnd",
        "outputId": "3314214d-11a4-4399-d820-4c11f5d423b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  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"
          ]
        }
      ],
      "source": [
        "!pip install -q bitsandbytes datasets accelerate loralib\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358,
          "referenced_widgets": [
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          ]
        },
        "id": "DpYr24pR8T_0",
        "outputId": "a4fb53a1-b116-43e3-cdc5-5f14b21ee9d7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "33fbaab8ca5448d49150f3621bae346b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi -L"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7650BSUPZh0Y",
        "outputId": "7f0cf85b-1bc7-4ae5-f13d-7795d889510b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: Tesla T4 (UUID: GPU-dc7b2ef1-71a3-c146-a661-8e53c0863ead)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOtwYRI3zzXI"
      },
      "source": [
        "### Setup the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cg3fiQOvmI3Q"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"bigscience/bloom-7b1\", \n",
        "    load_in_8bit=True, \n",
        "    device_map='auto',\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bigscience/bloom-7b1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fTSZntA1iUG"
      },
      "source": [
        "### Freezing the original weights\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-gy-LxM0yAi"
      },
      "outputs": [],
      "source": [
        "for param in model.parameters():\n",
        "  param.requires_grad = False  # freeze the model - train adapters later\n",
        "  if param.ndim == 1:\n",
        "    # cast the small parameters (e.g. layernorm) to fp32 for stability\n",
        "    param.data = param.data.to(torch.float32)\n",
        "\n",
        "model.gradient_checkpointing_enable()  # reduce number of stored activations\n",
        "model.enable_input_require_grads()\n",
        "\n",
        "class CastOutputToFloat(nn.Sequential):\n",
        "  def forward(self, x): return super().forward(x).to(torch.float32)\n",
        "model.lm_head = CastOutputToFloat(model.lm_head)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwOTr7B3NlM3"
      },
      "source": [
        "### Setting up the LoRa Adapters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4W1j6lxaNnxC"
      },
      "outputs": [],
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4iwHGzKBN6wk"
      },
      "outputs": [],
      "source": [
        "from peft import LoraConfig, get_peft_model \n",
        "\n",
        "config = LoraConfig(\n",
        "    r=16, #attention heads\n",
        "    lora_alpha=32, #alpha scaling\n",
        "    # target_modules=[\"q_proj\", \"v_proj\"], #if you know the \n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\" # set this for CLM or Seq2Seq\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data"
      ],
      "metadata": {
        "id": "-y6MRpkSfYGP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from datasets import load_dataset\n",
        "data = load_dataset(\"Abirate/english_quotes\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "16ce04b7-d19a-4d3e-dfc2-4c57d4446e5a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "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,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/Abirate--english_quotes to /root/.cache/huggingface/datasets/Abirate___json/Abirate--english_quotes-6e72855d06356857/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3337b33044f24f34bf4ee378971625e5"
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          },
          "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": "5cf40c0e456e4802b201a2994c91a18e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3cc144d6822649ef9093c621af4f73ca"
            }
          },
          "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": "9bffd648379c4d7f98a9adf581939fd0"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/Abirate___json/Abirate--english_quotes-6e72855d06356857/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d325cd4621843fba07329d193e47d7f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqWsWvZBm6Cx",
        "outputId": "0b6d5c62-2988-4b4f-8115-5f73f9ae392d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['quote', 'author', 'tags'],\n",
              "        num_rows: 2508\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X04_bqn5m7-_",
        "outputId": "2bdfffbe-50d4-4dde-99b6-ff7158710544"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'quote': '“Be yourself; everyone else is already taken.”',\n",
              " 'author': 'Oscar Wilde',\n",
              " 'tags': ['be-yourself',\n",
              "  'gilbert-perreira',\n",
              "  'honesty',\n",
              "  'inspirational',\n",
              "  'misattributed-oscar-wilde',\n",
              "  'quote-investigator']}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def merge_columns(example):\n",
        "    example[\"prediction\"] = example[\"quote\"] + \" ->: \" + str(example[\"tags\"])\n",
        "    return example\n",
        "\n",
        "data['train'] = data['train'].map(merge_columns)\n",
        "data['train'][\"prediction\"][:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
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      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"“Be yourself; everyone else is already taken.” ->: ['be-yourself', 'gilbert-perreira', 'honesty', 'inspirational', 'misattributed-oscar-wilde', 'quote-investigator']\",\n",
              " \"“I'm selfish, impatient and a little insecure. I make mistakes, I am out of control and at times hard to handle. But if you can't handle me at my worst, then you sure as hell don't deserve me at my best.” ->: ['best', 'life', 'love', 'mistakes', 'out-of-control', 'truth', 'worst']\",\n",
              " \"“Two things are infinite: the universe and human stupidity; and I'm not sure about the universe.” ->: ['human-nature', 'humor', 'infinity', 'philosophy', 'science', 'stupidity', 'universe']\",\n",
              " \"“So many books, so little time.” ->: ['books', 'humor']\",\n",
              " \"“A room without books is like a body without a soul.” ->: ['books', 'simile', 'soul']\"]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jL17IZCFhqw-",
        "outputId": "1fcf4d0d-4f17-4016-e6b6-c36db8dcaf02"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'quote': '“Be yourself; everyone else is already taken.”',\n",
              " 'author': 'Oscar Wilde',\n",
              " 'tags': ['be-yourself',\n",
              "  'gilbert-perreira',\n",
              "  'honesty',\n",
              "  'inspirational',\n",
              "  'misattributed-oscar-wilde',\n",
              "  'quote-investigator'],\n",
              " 'prediction': \"“Be yourself; everyone else is already taken.” ->: ['be-yourself', 'gilbert-perreira', 'honesty', 'inspirational', 'misattributed-oscar-wilde', 'quote-investigator']\"}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.map(lambda samples: tokenizer(samples['prediction']), batched=True)"
      ],
      "metadata": {
        "id": "v2huC6dMh5vE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "dNIP6Z8Wh-W3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QdjWif4CVXR6"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AQ_HCYruWIHU"
      },
      "outputs": [],
      "source": [
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model, \n",
        "    train_dataset=data['train'],\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=4, \n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=100, \n",
        "        max_steps=200, \n",
        "        learning_rate=2e-4, \n",
        "        fp16=True,\n",
        "        logging_steps=1, \n",
        "        output_dir='outputs'\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Duak7T_B3VpJ"
      },
      "source": [
        "## Share adapters on the 🤗 Hub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"huggingface923/bloom-7b1-lora-tagger\",\n",
        "                  use_auth_token=True,\n",
        "                  commit_message=\"basic training\",\n",
        "                  private=True)"
      ],
      "metadata": {
        "id": "cV8ZwAeDklMe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S65GcxNGA9kz"
      },
      "source": [
        "## Load adapters from the Hub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "peft_model_id = \"huggingface923/bloom-7b1-lora-tagger\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, 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)"
      ],
      "metadata": {
        "id": "yVRL5VyykocL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHYljmTjj5wX"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch = tokenizer(\"“Training models with PEFT and LoRa is cool” ->: \", return_tensors='pt')\n",
        "\n",
        "with torch.cuda.amp.autocast():\n",
        "  output_tokens = model.generate(**batch, max_new_tokens=50)\n",
        "\n",
        "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "id": "B0pZU2i0krzS"
      },
      "execution_count": null,
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    }
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