Language/12_04_TRL_PPO.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/peisuke/generative_ai_notebooks/blob/main/Language/12_04_TRL_PPO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s5yCkJCyFHgf",
        "outputId": "4e842f5c-74d3-4d2c-e02b-a30c5b79a90f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Googleドライブのマウント\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RIWwcSbOFc1g",
        "outputId": "9448a5e0-fd97-457d-dd36-6dba047a6cb6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.8/209.8 MB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.5/20.5 MB\u001b[0m \u001b[31m81.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m16.8 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[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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",
            "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# パッケージのインストール\n",
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 sentencepiece xformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Z-xdgufJLTF",
        "outputId": "661350db-aaf4-4311-8325-1fcc8d03a1ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "# HuggingFaceのログイン\n",
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "P3Nmg6pLF_Ly"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from tqdm import tqdm\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, BitsAndBytesConfig\n",
        "from peft import AutoPeftModelForCausalLM, AutoPeftModelForSequenceClassification\n",
        "from trl import PPOConfig, PPOTrainer, AutoModelForCausalLMWithValueHead\n",
        "from transformers import pipeline\n",
        "from datasets import load_dataset\n",
        "from trl.core import LengthSampler\n",
        "\n",
        "from peft import LoraConfig, PeftModel, PeftConfig, prepare_model_for_kbit_training, get_peft_model\n",
        "from transformers import AutoTokenizer, TrainingArguments, AutoModelForCausalLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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        "colab": {
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        },
        "id": "dozO5cV50H2a",
        "outputId": "c1d3090c-580d-4757-dd20-4e95e0b7100c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c5dc884d485840b5858541aed6ca057b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:102: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n",
            "WARNING:root:A <class 'peft.peft_model.PeftModelForCausalLM'> model is loaded from '/content/drive/MyDrive/AI/RLHF/merged_model', and no v_head weight is found. This IS expected if you are not resuming PPO training.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92e85e5db2f44d71bfe2287437418424"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8bb3a6f112e4bc88951c9201b26b709"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eefd18ddea12494e9896ba29aedd4a04"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b596864250a466daf36162f64567412"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model_name=\"/content/drive/MyDrive/AI/RLHF/merged_model\"\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "model = AutoModelForCausalLMWithValueHead.from_pretrained(\n",
        "    model_name,\n",
        "    torch_dtype=torch.float16,\n",
        "    load_in_8bit=True,\n",
        "    device_map={\"\": 0},\n",
        "    peft_config=lora_config\n",
        ")\n",
        "model.to(\"cuda\")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    \"meta-llama/Llama-2-7b-chat-hf\"\n",
        ")\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        },
        "id": "fDI2yfIt_7Ei",
        "outputId": "5ed3eefa-8db6-431c-d43a-29a2e034c503"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/326 [00:00<?, ?B/s]"
            ],
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              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/3.23M [00:00<?, ?B/s]"
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              "version_major": 2,
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              "model_id": "c68b67ed0c024ae3b14409865252b5aa"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "32d85175804942e2a4ce629c70664ef3"
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          "metadata": {}
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      ],
      "source": [
        "rm_tokenizer = AutoTokenizer.from_pretrained(\"cyberagent/open-calm-small\")\n",
        "rm_tokenizer.pad_token = rm_tokenizer.eos_token\n",
        "\n",
        "sentiment_pipe = pipeline(\n",
        "    \"sentiment-analysis\",\n",
        "    model=\"/content/drive/MyDrive/AI/RLHF/reward_model\",\n",
        "    device_map={\"\": 0},\n",
        "    model_kwargs={\"load_in_8bit\": True},\n",
        "    tokenizer=rm_tokenizer,\n",
        "    return_token_type_ids=False,\n",
        ")\n",
        "sentiment_pipe.model.config.pad_token_id = rm_tokenizer.eos_token_id\n",
        "\n",
        "sent_kwargs = {\n",
        "    \"return_all_scores\": True,\n",
        "    \"function_to_apply\": \"none\",\n",
        "    \"batch_size\": 16,\n",
        "    \"truncation\": True,\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        "outputId": "d990eab2-9f8f-44b3-f327-0342be29cc7a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/628 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
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              "version_major": 2,
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          "data": {
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              "Downloading data:   0%|          | 0.00/18.4M [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/49332 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e18af964b6274d56870276a2c81d43ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/49332 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "798a74484ef347bc90e2a48c18cbf4e3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = load_dataset(\"fujiki/japanese_hh-rlhf-49k\", split=\"train\")\n",
        "dataset = dataset.rename_column(\"instruction\", \"query\")\n",
        "dataset = dataset.remove_columns([\"input\", \"output\", \"index\"])\n",
        "\n",
        "def tokenize(sample):\n",
        "    sample[\"input_ids\"] = tokenizer.encode(sample[\"query\"])\n",
        "    return sample\n",
        "\n",
        "dataset = dataset.map(tokenize)\n",
        "dataset.set_format(type=\"torch\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "H9zbl1noGtCD"
      },
      "outputs": [],
      "source": [
        "def collator(data):\n",
        "  return dict((key,[d[key] for d in data]) for key in data[0])\n",
        "\n",
        "# コンフィグの準備\n",
        "config = PPOConfig(\n",
        "    learning_rate=1.41e-5,\n",
        "    batch_size=1,\n",
        "    mini_batch_size=1,\n",
        ")\n",
        "\n",
        "ppo_trainer = PPOTrainer(\n",
        "    config,\n",
        "    model,\n",
        "    ref_model=None,\n",
        "    tokenizer=tokenizer,\n",
        "    dataset=dataset,\n",
        "    data_collator=collator\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "7S7s7CQJGugi"
      },
      "outputs": [],
      "source": [
        "generation_kwargs = {\n",
        "    \"min_length\": 10,\n",
        "    \"max_length\": 100,\n",
        "    \"top_k\": 0.0,\n",
        "    \"top_p\": 1.0,\n",
        "    \"do_sample\": True,\n",
        "    \"pad_token_id\": tokenizer.eos_token_id,\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqpfpIUmGwjw",
        "outputId": "54d177ab-ceac-4df8-9303-4ff23acfde11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_classification.py:104: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "5it [01:21, 15.59s/it]/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_trainer.py:1133: UserWarning: KL divergence is starting to become negative: -1.11 - this might be a precursor for failed training. sometimes this happens because the generation kwargs are not correctly set. Please make sure that the generation kwargs are set correctly, or review your training hyperparameters.\n",
            "  warnings.warn(\n",
            "6it [01:44, 18.04s/it]/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_trainer.py:1133: UserWarning: KL divergence is starting to become negative: -1.33 - this might be a precursor for failed training. sometimes this happens because the generation kwargs are not correctly set. Please make sure that the generation kwargs are set correctly, or review your training hyperparameters.\n",
            "  warnings.warn(\n",
            "8it [02:23, 19.15s/it]/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_trainer.py:1133: UserWarning: KL divergence is starting to become negative: -1.81 - this might be a precursor for failed training. sometimes this happens because the generation kwargs are not correctly set. Please make sure that the generation kwargs are set correctly, or review your training hyperparameters.\n",
            "  warnings.warn(\n",
            "9it [02:45, 20.08s/it]/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_trainer.py:1133: UserWarning: KL divergence is starting to become negative: -1.48 - this might be a precursor for failed training. sometimes this happens because the generation kwargs are not correctly set. Please make sure that the generation kwargs are set correctly, or review your training hyperparameters.\n",
            "  warnings.warn(\n",
            "10it [03:00, 18.43s/it]/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "10it [03:23, 20.38s/it]\n"
          ]
        }
      ],
      "source": [
        "for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):\n",
        "    query_tensors = batch[\"input_ids\"]\n",
        "\n",
        "    #### Get response from SFTModel\n",
        "    response_tensors = ppo_trainer.generate(query_tensors, **generation_kwargs)\n",
        "    batch[\"response\"] = [tokenizer.decode(r.squeeze()) for r in response_tensors]\n",
        "\n",
        "    #### Compute reward score\n",
        "    texts = [q + r for q, r in zip(batch[\"query\"], batch[\"response\"])]\n",
        "    pipe_outputs = sentiment_pipe(texts, **sent_kwargs)\n",
        "    rewards = [torch.tensor(output[0][\"score\"]) for output in pipe_outputs]\n",
        "\n",
        "    #### Run PPO step\n",
        "    stats = ppo_trainer.step(query_tensors, response_tensors, rewards)\n",
        "    ppo_trainer.log_stats(stats, batch, rewards)\n",
        "\n",
        "    if epoch >= 10:\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Opwl0QM5UW6d"
      },
      "outputs": [],
      "source": [
        "ppo_trainer.save_pretrained(f\"/content/drive/MyDrive/AI/RLHF/ppo_model\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "25lfZTtotRBk"
      },
      "execution_count": null,
      "outputs": []
    }
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