DPO.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n",
        "!pip install -q datasets bitsandbytes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5UP1MPGiMkpM",
        "outputId": "79d3ff9e-1169-4db6-812c-2460cb225485"
      },
      "execution_count": 1,
      "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",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.1/44.1 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m310.9/310.9 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m90.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m36.8 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[31m10.5 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[31m14.8 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[31m12.4 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[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (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[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m18.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fprewjVeExh8",
        "outputId": "acea5880-f37d-47c6-b712-cd50c3ab38a7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Nov 24 18:39:01 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM4-40GB          Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   28C    P0              41W / 400W |      2MiB / 40960MiB |      0%      Default |\n",
            "|                                         |                      |             Disabled |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('Not using a high-RAM runtime')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8H8VsIXuhFAE",
        "outputId": "52f03b66-a716-434e-dcd6-d0811a22725a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your runtime has 89.6 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## https://huggingface.co/blog/dpo-trl"
      ],
      "metadata": {
        "id": "5biSk65YNz0y"
      }
    },
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        "id": "8K2PPVibMWsn",
        "outputId": "04310781-beeb-47c7-a59f-1afe746abd53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
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              "Generating train split: 0 examples [00:00, ? examples/s]"
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          "data": {
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            ],
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        {
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          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
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              "model_id": "3814faca1bb948bda3d23669f4a7c094"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "from typing import Dict\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "def return_prompt_and_responses(samples) -> Dict[str, str]:\n",
        "    return {\n",
        "        \"prompt\": [\n",
        "            \"Question: \" + question + \"\\n\\nAnswer: \"\n",
        "            for question in samples[\"question\"]\n",
        "        ],\n",
        "        \"chosen\": samples[\"response_j\"],   # rated better than k\n",
        "        \"rejected\": samples[\"response_k\"], # rated worse than j\n",
        "    }\n",
        "\n",
        "dataset = load_dataset(\n",
        "    \"lvwerra/stack-exchange-paired\",\n",
        "    split=\"train[:100]\",\n",
        "    data_dir=\"data/rl\"\n",
        ")\n",
        "original_columns = dataset.column_names\n",
        "model_name = \"meta-llama/Llama-3.1-8B-Instruct\"\n",
        "\n",
        "processed_dataset = dataset.map(\n",
        "    return_prompt_and_responses,\n",
        "    batched=True,\n",
        "    remove_columns=original_columns\n",
        ")\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    \"\"\"\n",
        "    Tokenize the 'prompt' column in the dataset.\n",
        "    \"\"\"\n",
        "    return tokenizer(examples[\"prompt\"], truncation=True, padding=\"max_length\", max_length=512)\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, token = \"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\")\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "\n",
        "tokenized_dataset = processed_dataset.map(tokenize_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Convert processed dataset to Pandas DataFrame\n",
        "processed_df = pd.DataFrame(processed_dataset)\n",
        "print(processed_df.head())\n",
        "\n",
        "# Convert tokenized dataset to Pandas DataFrame\n",
        "tokenized_df = pd.DataFrame(tokenized_dataset)\n",
        "print(tokenized_df.head())"
      ],
      "metadata": {
        "id": "9eXUbms12Fzv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "add96ac0-da4c-4035-f215-b9c049b6e8db"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                              prompt  \\\n",
            "0  Question: I'm writing a code using Java Swing ...   \n",
            "1  Question: I'm writing a code using Java Swing ...   \n",
            "2  Question: I am a senior Siebel CRM developer h...   \n",
            "3  Question: i want to write a shape with \" \\* \" ...   \n",
            "4  Question: i want to write a shape with \" \\* \" ...   \n",
            "\n",
            "                                              chosen  \\\n",
            "0  The code you are showing does exactly one thin...   \n",
            "1  From what I understand, essentially you want t...   \n",
            "2  After some research and not reaching to any so...   \n",
            "3  I see a\\n\\n```\\nwhile (Height > 0)\\n\\n```\\n\\ns...   \n",
            "4  I see a\\n\\n```\\nwhile (Height > 0)\\n\\n```\\n\\ns...   \n",
            "\n",
            "                                            rejected  \n",
            "0  When you do this:\\n\\n```\\nbutton1.addActionLis...  \n",
            "1  When you do this:\\n\\n```\\nbutton1.addActionLis...  \n",
            "2  You may need to raise the case with SFMC suppo...  \n",
            "3  It's better to rewrite. When you do, decouple ...  \n",
            "4  ```\\nvoid WriteStars(int Width,int Height)\\n{\\...  \n",
            "                                              prompt  \\\n",
            "0  Question: I'm writing a code using Java Swing ...   \n",
            "1  Question: I'm writing a code using Java Swing ...   \n",
            "2  Question: I am a senior Siebel CRM developer h...   \n",
            "3  Question: i want to write a shape with \" \\* \" ...   \n",
            "4  Question: i want to write a shape with \" \\* \" ...   \n",
            "\n",
            "                                              chosen  \\\n",
            "0  The code you are showing does exactly one thin...   \n",
            "1  From what I understand, essentially you want t...   \n",
            "2  After some research and not reaching to any so...   \n",
            "3  I see a\\n\\n```\\nwhile (Height > 0)\\n\\n```\\n\\ns...   \n",
            "4  I see a\\n\\n```\\nwhile (Height > 0)\\n\\n```\\n\\ns...   \n",
            "\n",
            "                                            rejected  \\\n",
            "0  When you do this:\\n\\n```\\nbutton1.addActionLis...   \n",
            "1  When you do this:\\n\\n```\\nbutton1.addActionLis...   \n",
            "2  You may need to raise the case with SFMC suppo...   \n",
            "3  It's better to rewrite. When you do, decouple ...   \n",
            "4  ```\\nvoid WriteStars(int Width,int Height)\\n{\\...   \n",
            "\n",
            "                                           input_ids  \\\n",
            "0  [128000, 14924, 25, 358, 2846, 4477, 264, 2082...   \n",
            "1  [128000, 14924, 25, 358, 2846, 4477, 264, 2082...   \n",
            "2  [128000, 14924, 25, 358, 1097, 264, 10195, 866...   \n",
            "3  [128000, 14924, 25, 602, 1390, 311, 3350, 264,...   \n",
            "4  [128000, 14924, 25, 602, 1390, 311, 3350, 264,...   \n",
            "\n",
            "                                      attention_mask  \n",
            "0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
            "1  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
            "2  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
            "3  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
            "4  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenized_dataset.column_names)"
      ],
      "metadata": {
        "id": "NzwanZ0-ePKl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edd67c83-244c-4945-e786-38ebd2923a34"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['prompt', 'chosen', 'rejected', 'input_ids', 'attention_mask']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "model_name = \"meta-llama/Llama-3.1-8B-Instruct\"\n",
        "# load the base model in 4-bit quantization\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",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,        # \"meta-llama/Llama-2-7b-hf\"\n",
        "    quantization_config=bnb_config,\n",
        "    device_map={\"\": 0},\n",
        "    trust_remote_code=True,\n",
        "    token = \"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\"\n",
        ")\n",
        "base_model.config.use_cache = False\n",
        "\n"
      ],
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        "id": "1s5FUQNvNRCJ",
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            "e1db6a8addf04b6684912c2215b4d8af",
            "fd345ba016d54822872033870e3f6d45",
            "ec62b0dddf6046488e1bb01fa3197bce",
            "27b23f3972e2483086cbfc11b6f01434",
            "6e3279ca2c23460aa2a29786f11e4489",
            "301eb592753e474795600c8b9656d311",
            "0fe49ff934a24c56a901d9db46f5193f",
            "34b090d775784611b32a8c14bba0ad1c",
            "e3c53fcc99c44a499d16f5a46d668475",
            "95add7dcd51648cf94853504b59fb043",
            "87c0c34a4d0a4561bbff292ee4dd16e8",
            "1e8cc669c62443e6b618b1c562c87160",
            "0af9cb05421945f4b391339e651f08ed",
            "a70443fcebc14695bac8656dca5eb146",
            "4fdca6fdc9f141e4a00cee8aa307d6ed",
            "607523424a224a178661580efb14b554"
          ]
        },
        "outputId": "aa3d385f-6604-42c4-f5ca-73339284d602"
      },
      "execution_count": 7,
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# add LoRA layers on top of the quantized base model\n",
        "from peft import LoraConfig\n",
        "\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "lora_r = 64\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=[\n",
        "        \"gate_proj\",\n",
        "        \"up_proj\",\n",
        "        \"down_proj\",\n",
        "        \"q_proj\",\n",
        "        \"k_proj\",\n",
        "        \"v_proj\",\n",
        "        \"o_proj\"\n",
        "    ]\n",
        ") #It is suggest we target Linear modules only. From above cell we can see the layers details\n"
      ],
      "metadata": {
        "id": "6xdDg-ckOHTm"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "output_dir = \"./results\"\n",
        "per_device_train_batch_size = 2\n",
        "gradient_accumulation_steps = 4\n",
        "optim = \"paged_adamw_32bit\"\n",
        "evaluation_strategy=\"epoch\"\n",
        "save_strategy=\"no\"\n",
        "logging_steps = 10\n",
        "learning_rate = 2e-4\n",
        "warmup_ratio = 0.03\n",
        "lr_scheduler_type = \"constant\"\n",
        "epochs = 1\n",
        "\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    fp16=True,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        "    gradient_checkpointing=True,\n",
        "    report_to=\"none\",\n",
        "    num_train_epochs=epochs,\n",
        "    evaluation_strategy =\"no\",\n",
        "    save_strategy= save_strategy\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "C3kL9i3sOtPb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87ce5029-a6fd-4eb0-ea85-4e75f1c98efe"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.column_names)"
      ],
      "metadata": {
        "id": "fBvL-VvEb5JI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd8190c0-4b2d-49fd-90ed-1e21307ff48e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['qid', 'question', 'date', 'metadata', 'response_j', 'response_k']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "max_seq_length = 512\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=base_model,\n",
        "    train_dataset=tokenized_dataset,\n",
        "    peft_config=peft_config,\n",
        "    max_seq_length=None,\n",
        "    args=training_arguments,\n",
        ")"
      ],
      "metadata": {
        "id": "ao627KvvO0b6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b2439d0-2d78-4472-b123-ca0678631fd7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:309: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for name, module in trainer.model.named_modules():\n",
        "    if \"norm\" in name:\n",
        "        module = module.to(torch.float32)"
      ],
      "metadata": {
        "id": "5mGgV1waVOK5"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()\n",
        "trainer.model.save_pretrained(\"results\")\n",
        "tokenizer.save_pretrained(\"results\")"
      ],
      "metadata": {
        "id": "Um-gvFMaVPTF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "8455d0bf-da3f-4c1a-c2d0-f2fe66d7d378"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='12' max='12' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [12/12 00:35, 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.434500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:716: UserWarning: Unable to fetch remote file due to the following error 401 Client Error. (Request ID: Root=1-674374f0-4da88b14231845ce761cc833;c024c0de-451f-4922-bc84-b5dc7a8f1daa)\n",
            "\n",
            "Cannot access gated repo for url https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/resolve/main/config.json.\n",
            "Access to model meta-llama/Llama-3.1-8B-Instruct is restricted. You must have access to it and be authenticated to access it. Please log in. - silently ignoring the lookup for the file config.json in meta-llama/Llama-3.1-8B-Instruct.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:246: UserWarning: Could not find a config file in meta-llama/Llama-3.1-8B-Instruct - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('results/tokenizer_config.json',\n",
              " 'results/special_tokens_map.json',\n",
              " 'results/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import AutoPeftModelForCausalLM\n",
        "model = AutoPeftModelForCausalLM.from_pretrained(\n",
        "    \"results\", # location of saved SFT model\n",
        "    low_cpu_mem_usage=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    load_in_4bit=True,\n",
        "    is_trainable=True,\n",
        ")\n",
        "#model_ref = AutoPeftModelForCausalLM.from_pretrained(\n",
        "#    \"results/fine_tuned_model\",  # same model as the main one\n",
        "#    low_cpu_mem_usage=True,\n",
        "#    torch_dtype=torch.float16,\n",
        "#    load_in_4bit=True,\n",
        "#)"
      ],
      "metadata": {
        "id": "lOAZYFyEPBNN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "f371dd8b664c442d8d658e310e0abc2b",
            "e988d4fcdef34842af0cf96e974d5468",
            "109b04ab5e5e40a49d22f8aaa71f81f8",
            "b39d36020460492a88963b2bee0b3d00",
            "e2dfee573c974650b246b1ea806374f7",
            "eefda9df052640e3aa413a3ee4c1c3f3",
            "fb45ab813c014c52b28d8eb8703de756",
            "2b8dcd5452264dbaa01cd1197628c80e",
            "d4d049aa49cb493ab85a0ab572f9301d",
            "7e9c447c59bf44a88b221786181b9625",
            "697447d1077042feb03cadf54422d319"
          ]
        },
        "outputId": "e7017fa4-77f3-496b-a39b-1ed0c6e8ccfc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f371dd8b664c442d8d658e310e0abc2b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade trl"
      ],
      "metadata": {
        "id": "sY4RzlfMusDj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b60108a-8d6c-47dc-ba2a-b477772a346b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import DPOTrainer\n",
        "from trl import DPOConfig\n",
        "\n",
        "from transformers import TrainingArguments\n",
        "training_args = DPOConfig(output_dir=\"results\", beta=0.1,per_device_train_batch_size=1,fp16=True,)\n",
        "\n",
        "\n",
        "\n",
        "dpo_trainer = DPOTrainer(\n",
        "    model,\n",
        "    args=training_args,\n",
        "    beta=0.1,\n",
        "    train_dataset=tokenized_dataset,\n",
        "    tokenizer=tokenizer,\n",
        "    peft_config=peft_config,\n",
        ")\n",
        "dpo_trainer.train()\n",
        "dpo_trainer.save_model()"
      ],
      "metadata": {
        "id": "vdOVxTgtSx8O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860,
          "referenced_widgets": [
            "21b7f9ce30f543f2991629425bed8e5d",
            "603f54b6ab1f4a6a8a4a33dafaeed687",
            "443959d9ff0d48a9a1158b1bddf9131a",
            "4fdf3d02151545aabd272b309b37f540",
            "12413a03119f43d2bf8434f92ee12c33",
            "6f7d597d8f554580b1c770c6ce2be286",
            "15037b8d99a34c5f835853914f6759e0",
            "342dbc5d2e4e4288a8c59e36c9f9002a",
            "2a6b26e2b50a464586135c65aebdf013",
            "4996f2d63c5d4c6d8562cbe868b78438",
            "d6f50e8fed174a7c8d46b43ff9bf4d2c",
            "75ab25ce1563463fbec7d6320ea6c09d",
            "d845c1cb0e1e4c1db972bdad0dc44f3a",
            "58bd55ca90c545d180d4a9154ab358a0",
            "d0ed669f7e9943cfaff5fe50e595380d",
            "7ab360854dbc44798f74e9351adc657f",
            "78a42998a3b54414a3925f0683158fce",
            "85b13608efb54592a0c6a4f78d7c191d",
            "d0c6926755d54454afd8d0c74952ea11",
            "7f75ef6ddcf8481891a13d3349305bee",
            "23221c4a0f564bc08417aeffe86eb56a",
            "a587aca946394834bc7b467b19199a1a",
            "0a149125365e4b1596a707854508a67c",
            "16836ec45f5f408296e866ffad8d47c1",
            "c96ea12a8d0e40e994efb2624c5f69cb",
            "3f0c3e38231a460a906ebe9e64eacaf7",
            "da7b5b460e5a4ebba061014b7326b883",
            "c7b666c1cea147179615347fb2385d65",
            "f1729483a3f244788aecbf53509a1d8d",
            "755d8aa59b2f49da9d699a7f5756aca1",
            "8b4b80b4f74c4a3ea6fb9a86817a70dd",
            "c373bf78f55d4756bb6c8be76727d57d",
            "5e37aaaeb8684fac86be7f3684fc1f67"
          ]
        },
        "outputId": "a8d9d367-d8b4-4051-d48c-4e82283fdaad"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py:340: UserWarning: Merge lora module to 4-bit linear may get different generations due to rounding errors.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting prompt from train dataset:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21b7f9ce30f543f2991629425bed8e5d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Applying chat template to train dataset:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75ab25ce1563463fbec7d6320ea6c09d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tokenizing train dataset:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a149125365e4b1596a707854508a67c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.18.7"
            ]
          },
          "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-20241124_184929-u66twqeg</code>"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/m-tannistha/huggingface/runs/u66twqeg' target=\"_blank\">results</a></strong> to <a href='https://wandb.ai/m-tannistha/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/m-tannistha/huggingface' target=\"_blank\">https://wandb.ai/m-tannistha/huggingface</a>"
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          },
          "metadata": {}
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/m-tannistha/huggingface/runs/u66twqeg' target=\"_blank\">https://wandb.ai/m-tannistha/huggingface/runs/u66twqeg</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py:452: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
            "  warnings.warn(\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='2' max='300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [  2/300 : < :, Epoch 0.01/3]\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",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "OutOfMemoryError",
          "evalue": "CUDA out of memory. Tried to allocate 926.00 MiB. GPU 0 has a total capacity of 39.56 GiB of which 2.81 MiB is free. Process 49976 has 39.46 GiB memory in use. Of the allocated memory 38.48 GiB is allocated by PyTorch, and 481.88 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-bd8f674a4ab8>\u001b[0m in \u001b[0;36m<cell line: 17>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mpeft_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpeft_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m )\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mdpo_trainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0mdpo_trainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   2121\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2122\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2123\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   2124\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2479\u001b[0m                     )\n\u001b[1;32m   2480\u001b[0m                     \u001b[0;32mwith\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2481\u001b[0;31m                         \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2483\u001b[0m                     if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m   3577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3578\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3579\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3581\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/trl/trainer/dpo_trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m   1369\u001b[0m         \u001b[0mcompute_loss_context_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mamp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_peft_has_been_casted_to_bf16\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnullcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1370\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1371\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_batch_loss_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_eval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1372\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m         \u001b[0;31m# Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/trl/trainer/dpo_trainer.py\u001b[0m in \u001b[0;36mget_batch_loss_metrics\u001b[0;34m(self, model, batch, train_eval)\u001b[0m\n\u001b[1;32m   1328\u001b[0m             \u001b[0mref_rejected_logps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ref_rejected_logps\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1329\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1330\u001b[0;31m             \u001b[0mref_chosen_logps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref_rejected_logps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_ref_log_probs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1332\u001b[0m         losses, chosen_rewards, rejected_rewards = self.dpo_loss(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/trl/trainer/dpo_trainer.py\u001b[0m in \u001b[0;36mcompute_ref_log_probs\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref_model\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnull_ref_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m                     \u001b[0mref_model_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenated_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m                 \u001b[0mref_model_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenated_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/trl/trainer/dpo_trainer.py\u001b[0m in \u001b[0;36mconcatenated_forward\u001b[0;34m(self, model, batch)\u001b[0m\n\u001b[1;32m   1272\u001b[0m         \u001b[0;31m# Compute the log probabilities of the labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1273\u001b[0m         \u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mloss_mask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m  \u001b[0;31m# dummy token; we'll ignore the losses on these tokens later\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1274\u001b[0;31m         \u001b[0mper_token_logps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_softmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1275\u001b[0m         \u001b[0mper_token_logps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mloss_mask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1276\u001b[0m         \u001b[0mall_logps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mper_token_logps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 926.00 MiB. GPU 0 has a total capacity of 39.56 GiB of which 2.81 MiB is free. Process 49976 has 39.46 GiB memory in use. Of the allocated memory 38.48 GiB is allocated by PyTorch, and 481.88 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
          ]
        }
      ]
    }
  ]
}