{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m8t6tkjuuONX"
      },
      "source": [
        "## PaliGemma Fine-tuning\n",
        "\n",
        "In this notebook, we will fine-tune [pretrained PaliGemma](https://huggingface.co/google/paligemma2-3b-pt-448) on a small split of [VQAv2](https://huggingface.co/datasets/HuggingFaceM4/VQAv2) dataset. Let's get started by installing necessary libraries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EB0gv8OzHfLV",
        "outputId": "9de07e75-ddf4-4347-fc41-432a23774e2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m14.5 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[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m75.0 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[31m17.7 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",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q -U datasets bitsandbytes peft git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q_85okyYt1eo"
      },
      "source": [
        "We will authenticate to access the model using `notebook_login()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "4f0e85aa740146d3aca81588a0288031",
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            "ed2d3d1a700143d2a48e9a9b13bd1200",
            "40782cfc43a8437da5534feee03c6ba6",
            "b6fac3155dd140bc8e1b010270bc3cc2",
            "ca348c721475417582ed5018ed43151f",
            "3f07afac7c194db7a16167d177562a46",
            "5515d96f0c8947f0ad4b7f17eb7d63f6",
            "d703de12cf9d4f87aa6ec2cc52f1090a",
            "757bc788bd6842d28a9f889187ffb88e",
            "65f10d2456cb4ee1963fac050e4c34f7",
            "9335e48fe8ba4fe9b535b5ece1be6ff5",
            "80df5f3cd6c646808b09d99daed5bfd2"
          ]
        },
        "id": "NzJZSHD8tZZy",
        "outputId": "c01b2b6f-3c1e-45da-9fc0-f4f518bcca24"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f0e85aa740146d3aca81588a0288031",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_jUBDTEuw1j"
      },
      "source": [
        "Let's load the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "az5kdSbNpjgH",
        "outputId": "2d9f379c-eb31-45b0-b84c-79c2a2577d01"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: 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"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "ds = load_dataset('merve/vqav2-small', split=\"validation\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "wN1c9Aqhqt47"
      },
      "outputs": [],
      "source": [
        "split_ds = ds.train_test_split(test_size=0.9) # we'll use a very small split for demo\n",
        "train_ds = split_ds[\"test\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNJW2ty4yy4L",
        "outputId": "f76414b2-8f37-48ae-d369-b977323fa892"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['multiple_choice_answer', 'question', 'image'],\n",
              "    num_rows: 19292\n",
              "})"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_ds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hi_Y1blXwA04"
      },
      "source": [
        "Our dataset is a very general one and similar to many datasets that PaliGemma was trained with. In this case, we do not need to fine-tune the image encoder, the multimodal projector but we will only fine-tune the text decoder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Zya_PWM3uBWs"
      },
      "outputs": [],
      "source": [
        "from transformers import PaliGemmaProcessor\n",
        "model_id =\"google/paligemma2-3b-pt-224\" # or your favorite PaliGemma"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "8458933373264dbeb58d0b5ace4fd9c6",
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            "79e87175ffb949bd8cddf4577210a42d",
            "5aed84a20ac34f2b943d26d66decc88f",
            "3ca0e1427ac6477c9921929af7ff00d1",
            "a9a5503caf384b93bf987e5271a577d2"
          ]
        },
        "id": "iZRvrfUquH1y",
        "outputId": "34f12289-6ef4-49d9-9257-ad0328961190"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8458933373264dbeb58d0b5ace4fd9c6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import PaliGemmaForConditionalGeneration\n",
        "import torch\n",
        "device = \"cuda\"\n",
        "model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(device)\n",
        "\n",
        "for param in model.vision_tower.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "for param in model.multi_modal_projector.parameters():\n",
        "    param.requires_grad = False\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uCiVI-xUwSJm"
      },
      "source": [
        "Alternatively, if you want to do LoRA and QLoRA fine-tuning, you can run below cells to load the adapter either in full precision or quantized."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "c68f0fe7a6bb4060afcb05e3f6422288",
            "fef3c94897fc4ffa86f91aac7a45ac7f",
            "92881d2e3f1a438b92a389cc6022f7ad",
            "f518ab021bc648f188638fd168879edd",
            "1a29c71234d74f08b2645f9383fee126",
            "f8553ec713ea440eb0208a1012547988",
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            "daff4ba27c68441395aa5377111f30f1",
            "863090b3318e4e0186bd46d3d1479de4",
            "acae1751ff5d4293bb588c2d9c7ab851",
            "8859eb8d9c154cb79a302db1568768fa"
          ]
        },
        "id": "9AYeuyzNuJ9X",
        "outputId": "aaedd707-f694-4ba8-ba43-7ae2a3739e73"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c68f0fe7a6bb4060afcb05e3f6422288",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 11,876,352 || all params: 3,044,118,768 || trainable%: 0.3901\n"
          ]
        }
      ],
      "source": [
        "from transformers import BitsAndBytesConfig, PaliGemmaForConditionalGeneration\n",
        "from peft import get_peft_model, LoraConfig\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)\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",
        ")\n",
        "\n",
        "model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, device_map=\"auto\")#, quantization_config=bnb_config)\n",
        "model = get_peft_model(model, lora_config)\n",
        "model.print_trainable_parameters()\n",
        "#trainable params: 11,298,816 || all params: 2,934,634,224 || trainable%: 0.38501616002417344\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfxtN1iKRWXX"
      },
      "source": [
        "We need to take tokens to same dtype as model so need to store it as a variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "uGZ6FnioRWEc"
      },
      "outputs": [],
      "source": [
        "DTYPE = model.dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OsquATWQu2lJ"
      },
      "source": [
        "Load the processor to preprocess the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "wQ_gbnXARKz1"
      },
      "outputs": [],
      "source": [
        "processor = PaliGemmaProcessor.from_pretrained(model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZROnV-pu7rt"
      },
      "source": [
        "We will preprocess our examples. We need to prepare a prompt template and pass the text input inside, pass it with batches of images to processor. Then we will set the pad tokens and image tokens to -100 to let the model ignore them. We will pass our preprocessed input as labels to make the model learn how to generate responses."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "hdw3uBcNuGmw"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "image_token = processor.tokenizer.convert_tokens_to_ids(\"<image>\")\n",
        "def collate_fn(examples):\n",
        "  texts = [\"<image>answer en \" + example[\"question\"] for example in examples]\n",
        "  labels= [example['multiple_choice_answer'] for example in examples]\n",
        "  images = [example[\"image\"].convert(\"RGB\") for example in examples]\n",
        "  tokens = processor(text=texts, images=images, suffix=labels,\n",
        "                    return_tensors=\"pt\", padding=\"longest\")\n",
        "\n",
        "  tokens = tokens.to(DTYPE).to(device)\n",
        "  return tokens\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "logv0oLqwbIe"
      },
      "source": [
        "We will now initialize the `TrainingArguments`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Il7zKQO9uMPT"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "args=TrainingArguments(\n",
        "            num_train_epochs=2,\n",
        "            remove_unused_columns=False,\n",
        "            per_device_train_batch_size=1,\n",
        "            gradient_accumulation_steps=4,\n",
        "            warmup_steps=2,\n",
        "            learning_rate=2e-5,\n",
        "            weight_decay=1e-6,\n",
        "            adam_beta2=0.999,\n",
        "            logging_steps=100,\n",
        "            optim=\"adamw_hf\", # you can use paged optimizers like paged_adamw_8bit for QLoRA\n",
        "            save_strategy=\"steps\",\n",
        "            save_steps=1000,\n",
        "            save_total_limit=1,\n",
        "            output_dir=\"paligemma_vqav2\",\n",
        "            bf16=True,\n",
        "            report_to=[\"tensorboard\"],\n",
        "            dataloader_pin_memory=False\n",
        "        )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pR0EaGlwrDp"
      },
      "source": [
        "We can now start training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "CguCGDv1uNkF"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "        model=model,\n",
        "        train_dataset=train_ds ,\n",
        "        data_collator=collate_fn,\n",
        "        args=args\n",
        "        )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZX912_liP-Eh"
      },
      "source": [
        "LoRA with bsz of 2 works on A100 Colab. You can apply gradient accumulation (which is enabled in this notebook) to simulate larger batch sizes.\n",
        "Currently there's an issue with QLoRA, we are investigating and will solve soon."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9KFPQLrnF2Ha"
      },
      "outputs": [],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O9fMDEjXSSzF"
      },
      "outputs": [],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JohfxEJQjLBd"
      },
      "source": [
        "You can find steps to infer [here](https://colab.research.google.com/drive/100IQcvMvGm9y--oelbLfI__eHCoz5Ser?usp=sharing)."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "A100",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
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