{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3HSf7KnG48q"
      },
      "source": [
        "## PaliGemma 🤝 Direct Preference Optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emG7uw6IdhFs",
        "outputId": "07ce2f98-6c3d-47b5-eca5-2d58271dce9e"
      },
      "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",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhqsZVjzDmt4",
        "outputId": "5fbc28d8-76aa-4f12-a97f-b00db5ff0e6d"
      },
      "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[32m314.1/314.1 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.4/103.4 kB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m51.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m28.8 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[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 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[32m194.1/194.1 kB\u001b[0m \u001b[31m24.2 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[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for trl (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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/qgallouedec/trl.git@main"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nDXtvLWPTrbv",
        "outputId": "6b9a509a-4c0c-4928-ce59-1b697c32dfce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.6/251.6 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q -U peft accelerate bitsandbytes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VibvptIEHkuY"
      },
      "source": [
        "Let's load the model and the processor. First we need to login using `notebook_login` since PaliGemma is a gated model. For utmost security, you can create a fine-grained token with access to PaliGemma and pass that."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        },
        "id": "ulMkCsP7IY2K",
        "outputId": "8aa73adf-f6ae-4742-e84b-b4c092b6cdad"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "620ee05c533b465ab239668de96407a8",
              "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",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748,
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        },
        "id": "Y27eU6aqHkWU",
        "outputId": "ee0134d1-eb25-47c5-a24d-c99f32100d1b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "791667d3ddc64f459159fdd870344fdc",
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            ]
          },
          "metadata": {},
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        },
        {
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          },
          "metadata": {},
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        },
        {
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              "model_id": "be35fda566c94901a861f5c6046a86ac",
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              "model_id": "d73b48a0003b451a89cdcacb8fdbaeb8",
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              "model_id": "c691efefd8bc440bbedcaf90b1886b8d",
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          },
          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "`config.hidden_act` is ignored, you should use `config.hidden_activation` instead.\n",
            "Gemma's activation function will be set to `gelu_pytorch_tanh`. Please, use\n",
            "`config.hidden_activation` if you want to override this behaviour.\n",
            "See https://github.com/huggingface/transformers/pull/29402 for more details.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "29c984baa6e74f79a7bf002c5345d0ff",
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            },
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            ]
          },
          "metadata": {},
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        },
        {
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        },
        {
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              "model_id": "2068b87b63fe4ccfa84d2bb1ded542c5",
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              "model_id": "a563e9d93487442a92d67158ee2d1a2c",
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        },
        {
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              "model_id": "2f082fedc4fd4467836dd6e3e1b43e63",
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        },
        {
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              "model_id": "4ff55f49579140b0a814ae12f9d6fcaa",
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        {
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          },
          "metadata": {},
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0f96f7dd14d04f569d2118900c858537",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a45d435ba694ec48025794b69356e98",
              "version_major": 2,
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoModelForVision2Seq, AutoProcessor\n",
        "import torch\n",
        "\n",
        "model_id = \"google/paligemma-3b-pt-448\"\n",
        "model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.bfloat16)\n",
        "model_ref = AutoModelForVision2Seq.from_pretrained(model_id)\n",
        "processor = AutoProcessor.from_pretrained(model_id, do_image_splitting=False)\n",
        "tokenizer = processor.tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2yhXJ1LINE5"
      },
      "source": [
        "If you want to opt for parameter efficient fine-tuning, feel free to run below cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "id": "XEKmNS3_HrLl",
        "outputId": "b504fd90-8ff2-4029-e279-7b200a3d5eac"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d51ca282dafb4bae8dc093f52562ad9d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from trl import (\n",
        "    ModelConfig,\n",
        "    get_peft_config,\n",
        "    get_quantization_config\n",
        ")\n",
        "from peft import LoraConfig\n",
        "from transformers import BitsAndBytesConfig\n",
        "from trl import get_kbit_device_map\n",
        "from transformers import AutoModelForVision2Seq\n",
        "\n",
        "model_config = ModelConfig()\n",
        "# do not run below two lines if you're not doing QLoRA\n",
        "quantization_config = get_quantization_config(model_config)\n",
        "device_map=get_kbit_device_map()\n",
        "peft_config = get_peft_config(model_config)\n",
        "model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map=device_map)"
      ]
    },
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      "cell_type": "code",
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            },
            "text/plain": [
              "Generating train split:   0%|          | 0/78975 [00:00<?, ? examples/s]"
            ]
          },
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      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "ds = load_dataset(\"HuggingFaceH4/rlaif-v_formatted\")"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### Preprocessing\n",
        "\n",
        "PaliGemma doesn't have a chat template by default, so we need to add it to later preprocess."
      ]
    },
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      "execution_count": 4,
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      "source": [
        "import logging\n",
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "\n",
        "# paligemma has no chat template, we need to add it\n",
        "processor.chat_template = \"\"\"{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. {% for message in messages %}{% if message['role'] == 'user' %}USER: {% else %}ASSISTANT: {% endif %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}<image>{% endif %}{% endfor %}{% if message['role'] == 'user' %} {% else %}{{eos_token}}{% endif %}{% endfor %}{% if add_generation_prompt %}ASSISTANT: {% endif %}\"\"\"\n",
        "\n",
        "\n",
        "def process(row):\n",
        "  row[\"prompt\"] = processor.apply_chat_template(row[\"prompt\"], tokenize=False)\n",
        "  row[\"chosen\"] = processor.apply_chat_template(row[\"chosen\"], tokenize=False)\n",
        "  row[\"rejected\"] = processor.apply_chat_template(row[\"rejected\"], tokenize=False)\n",
        "  row[\"images\"][0] = row[\"images\"][0].convert(\"RGB\")\n",
        "  return row"
      ]
    },
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        "We'll do a very small run (as this notebook is made for education purposes)."
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
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            "text/plain": [
              "Map (num_proc=8):   0%|          | 0/39487 [00:00<?, ? examples/s]"
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      "source": [
        "train_dataset = ds[\"train\"].train_test_split(test_size=0.5)[\"train\"]\n",
        "eval_dataset = ds[\"test\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q2eHSY7TZoR1"
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      "source": [
        "from trl import DPOConfig, DPOTrainer, ModelConfig\n",
        "\n",
        "training_args = DPOConfig(output_dir=\"/content/outs\",\n",
        "                          per_device_train_batch_size=8,\n",
        "                          num_train_epochs=1)\n",
        "\n",
        "trainer = DPOTrainer(\n",
        "    model,\n",
        "    model_ref,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    tokenizer=processor,\n",
        "    dataset_num_proc=32,\n",
        "    # uncomment for peft\n",
        "    # peft_config=get_peft_config(model_config)\n",
        ")\n",
        "trainer.train()\n",
        "\n",
        "trainer.save_model(training_args.output_dir)"
      ]
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