Fine_tune_Llama_3_with_ORPO.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/syedzaidi-kiwi/Extreme-RAG/blob/main/Fine_tune_Llama_3_with_ORPO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-tune Llama 3 with ORPO\n",
        "\n",
        "❤️ Created by [@syedasad](https://www.linkedin.com/in/syed-asad-76815246/).\n",
        "\n",
        "You can run this notebook on Google Colab (I use an L4 GPU)."
      ],
      "metadata": {
        "id": "xjNdEQfFZsie"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b4tKGKGGQWBH"
      },
      "outputs": [],
      "source": [
        "!pip install -qqq -U transformers datasets accelerate peft trl bitsandbytes wandb --progress-bar off"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "outputId": "e4c288e1-2fa3-4717-d9dd-ae3ec0b91e3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmlabonne\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ],
      "source": [
        "import gc\n",
        "import os\n",
        "\n",
        "import torch\n",
        "import wandb\n",
        "from datasets import load_dataset\n",
        "from google.colab import userdata\n",
        "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        ")\n",
        "from trl import ORPOConfig, ORPOTrainer, setup_chat_format\n",
        "\n",
        "# Model\n",
        "base_model = \"meta-llama/Meta-Llama-3-8B\"\n",
        "new_model = \"OrpoLlama-3-8B\"\n",
        "\n",
        "# Defined in the secrets tab in Google Colab\n",
        "wb_token = userdata.get('wandb')\n",
        "wandb.login(key=wb_token)\n",
        "\n",
        "# Set torch dtype and attention implementation\n",
        "if torch.cuda.get_device_capability()[0] >= 8:\n",
        "    !pip install -qqq flash-attn\n",
        "    torch_dtype = torch.bfloat16\n",
        "    attn_implementation = \"flash_attention_2\"\n",
        "else:\n",
        "    torch_dtype = torch.float16\n",
        "    attn_implementation = \"eager\""
      ]
    },
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        {
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/50.9k [00:00<?, ?B/s]"
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            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
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              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
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      ],
      "source": [
        "# QLoRA config\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch_dtype,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        ")\n",
        "\n",
        "# LoRA config\n",
        "peft_config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=['up_proj', 'down_proj', 'gate_proj', 'k_proj', 'q_proj', 'v_proj', 'o_proj']\n",
        ")\n",
        "\n",
        "# Load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model)\n",
        "\n",
        "# Load model\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=\"auto\",\n",
        "    attn_implementation=attn_implementation\n",
        ")\n",
        "model, tokenizer = setup_chat_format(model, tokenizer)\n",
        "model = prepare_model_for_kbit_training(model)"
      ]
    },
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        "outputId": "48a298e6-6ca2-44b9-add3-4edba531f080"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.70k [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=16):   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "0db5b26c1c944d2b93788775983836d1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset_name = \"mlabonne/orpo-dpo-mix-40k\"\n",
        "dataset = load_dataset(dataset_name, split=\"all\")\n",
        "dataset = dataset.shuffle(seed=42).select(range(1000)) # Only use 1000 samples for quick demo\n",
        "\n",
        "def format_chat_template(row):\n",
        "    row[\"chosen\"] = tokenizer.apply_chat_template(row[\"chosen\"], tokenize=False)\n",
        "    row[\"rejected\"] = tokenizer.apply_chat_template(row[\"rejected\"], tokenize=False)\n",
        "    return row\n",
        "\n",
        "dataset = dataset.map(\n",
        "    format_chat_template,\n",
        "    num_proc= os.cpu_count(),\n",
        ")\n",
        "dataset = dataset.train_test_split(test_size=0.01)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AWDwJe7_Qqgb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570,
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        "outputId": "2a5d8940-c72b-4e45-ac89-24164f45e0e5"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/orpo_trainer.py:247: UserWarning: When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments we have set it for you, but you should do it yourself in the future.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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              "model_id": "0e5fbf1673694cbda1970b15ee49b187"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmlabonne\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.6"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240418_163822-4axpp5ux</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/mlabonne/huggingface/runs/4axpp5ux' target=\"_blank\">wise-plant-127</a></strong> to <a href='https://wandb.ai/mlabonne/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/mlabonne/huggingface' target=\"_blank\">https://wandb.ai/mlabonne/huggingface</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/mlabonne/huggingface/runs/4axpp5ux' target=\"_blank\">https://wandb.ai/mlabonne/huggingface/runs/4axpp5ux</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "The input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in torch.float16.\n",
            "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='123' max='123' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [123/123 2:03:21, Epoch 0/1]\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",
              "      <th>Validation Loss</th>\n",
              "      <th>Runtime</th>\n",
              "      <th>Samples Per Second</th>\n",
              "      <th>Steps Per Second</th>\n",
              "      <th>Rewards/chosen</th>\n",
              "      <th>Rewards/rejected</th>\n",
              "      <th>Rewards/accuracies</th>\n",
              "      <th>Rewards/margins</th>\n",
              "      <th>Logps/rejected</th>\n",
              "      <th>Logps/chosen</th>\n",
              "      <th>Logits/rejected</th>\n",
              "      <th>Logits/chosen</th>\n",
              "      <th>Nll Loss</th>\n",
              "      <th>Log Odds Ratio</th>\n",
              "      <th>Log Odds Chosen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.546200</td>\n",
              "      <td>1.681968</td>\n",
              "      <td>32.035100</td>\n",
              "      <td>0.312000</td>\n",
              "      <td>0.156000</td>\n",
              "      <td>-0.203300</td>\n",
              "      <td>-0.193025</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>-0.010275</td>\n",
              "      <td>-1.930252</td>\n",
              "      <td>-2.032999</td>\n",
              "      <td>-2.163935</td>\n",
              "      <td>-1.843625</td>\n",
              "      <td>1.605094</td>\n",
              "      <td>-0.768736</td>\n",
              "      <td>-0.126097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.233100</td>\n",
              "      <td>1.220901</td>\n",
              "      <td>31.998100</td>\n",
              "      <td>0.313000</td>\n",
              "      <td>0.156000</td>\n",
              "      <td>-0.141461</td>\n",
              "      <td>-0.132533</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>-0.008928</td>\n",
              "      <td>-1.325335</td>\n",
              "      <td>-1.414610</td>\n",
              "      <td>-2.147168</td>\n",
              "      <td>-1.844800</td>\n",
              "      <td>1.143643</td>\n",
              "      <td>-0.772585</td>\n",
              "      <td>-0.119247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.121000</td>\n",
              "      <td>1.124599</td>\n",
              "      <td>31.985000</td>\n",
              "      <td>0.313000</td>\n",
              "      <td>0.156000</td>\n",
              "      <td>-0.129975</td>\n",
              "      <td>-0.117504</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>-0.012470</td>\n",
              "      <td>-1.175042</td>\n",
              "      <td>-1.299747</td>\n",
              "      <td>-2.156506</td>\n",
              "      <td>-1.858128</td>\n",
              "      <td>1.045024</td>\n",
              "      <td>-0.795747</td>\n",
              "      <td>-0.168394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.066800</td>\n",
              "      <td>1.092966</td>\n",
              "      <td>31.919200</td>\n",
              "      <td>0.313000</td>\n",
              "      <td>0.157000</td>\n",
              "      <td>-0.125284</td>\n",
              "      <td>-0.112023</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>-0.013261</td>\n",
              "      <td>-1.120235</td>\n",
              "      <td>-1.252840</td>\n",
              "      <td>-2.193945</td>\n",
              "      <td>-1.898712</td>\n",
              "      <td>1.012895</td>\n",
              "      <td>-0.800711</td>\n",
              "      <td>-0.180113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:168: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "orpo_args = ORPOConfig(\n",
        "    learning_rate=8e-6,\n",
        "    lr_scheduler_type=\"linear\",\n",
        "    max_length=1024,\n",
        "    max_prompt_length=512,\n",
        "    beta=0.1,\n",
        "    per_device_train_batch_size=2,\n",
        "    per_device_eval_batch_size=2,\n",
        "    gradient_accumulation_steps=4,\n",
        "    optim=\"paged_adamw_8bit\",\n",
        "    num_train_epochs=1,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=0.2,\n",
        "    logging_steps=1,\n",
        "    warmup_steps=10,\n",
        "    report_to=\"wandb\",\n",
        "    output_dir=\"./results/\",\n",
        ")\n",
        "\n",
        "trainer = ORPOTrainer(\n",
        "    model=model,\n",
        "    args=orpo_args,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    eval_dataset=dataset[\"test\"],\n",
        "    peft_config=peft_config,\n",
        "    tokenizer=tokenizer,\n",
        ")\n",
        "trainer.train()\n",
        "trainer.save_model(new_model)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Flush memory\n",
        "del trainer, model\n",
        "gc.collect()\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "# Reload tokenizer and model\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model)\n",
        "fp16_model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model,\n",
        "    low_cpu_mem_usage=True,\n",
        "    return_dict=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "fp16_model, tokenizer = setup_chat_format(fp16_model, tokenizer)\n",
        "\n",
        "# Merge adapter with base model\n",
        "model = PeftModel.from_pretrained(fp16_model, new_model)\n",
        "model = model.merge_and_unload()"
      ],
      "metadata": {
        "id": "n6b3dCS6fL45"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(new_model, use_temp_dir=False)\n",
        "tokenizer.push_to_hub(new_model, use_temp_dir=False)"
      ],
      "metadata": {
        "id": "pvWUrbyNgEnX",
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          "data": {
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