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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hululuzhu/chinese-ai-writing-share/blob/main/further_finetune_example/gemma_lora_finetune.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gemma + LoRA = Finetune on Consumer level GPU\n",
        "- branched from my own [llama finetune repo](https://github.com/hululuzhu/llama-lora-chinese-couplet)\n",
        "- Last update: 03/02/2024\n",
        "- Contact: hululu.zhu@gmail.com\n",
        "- 03/02: Fix the IT version of Gemma tokens (e.g. start/end of user/model), correct format (the final new_line is also important)\n",
        "  ```\n",
        "  GEMMA_IT_FORMAT = \"\"\"<start_of_turn>user\n",
        "{user_question}<end_of_turn>\n",
        "<start_of_turn>model\n",
        "\"\"\"\n",
        "  ```\n",
        "\n",
        "\n",
        "Zero-shot Examples\n",
        "- after 10 mins maybe 500+ examples, cap max tokens, greedy, already seems promising\n",
        "- post-processing to match # of chinese chars\n",
        "- ideally a well trained model will know end of sentence (eos) itself\n",
        "\n"
      ],
      "metadata": {
        "id": "dc7pi1lcUGEK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prerequisites\n",
        "- Nvidia GPU, check if 10G HBM (High Bandwidth Memory) ram available\n",
        "- pip install software"
      ],
      "metadata": {
        "id": "JmJQAx79UhwI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi -L"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umlSDnCYaFLf",
        "outputId": "2b0578d4-a822-479b-ca73-46a9ebfa6150"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: Tesla T4 (UUID: GPU-784f4b93-126f-b9c0-5c89-f1f6683e910d)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CorDyq5oT4nm",
        "outputId": "178242b0-7dd9-435e-862b-6b9f4b30aeb7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for nvidia-ml-py3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Total memory: 16106127360\n",
            "Free memory: 15835529216\n",
            "Used memory: 270598144\n"
          ]
        }
      ],
      "source": [
        "!pip install -q nvidia-ml-py3\n",
        "import nvidia_smi\n",
        "nvidia_smi.nvmlInit()\n",
        "handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)\n",
        "# card id 0 hardcoded here, there is also a call to get all available card ids, so we could iterate\n",
        "info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)\n",
        "nvidia_smi.nvmlShutdown()\n",
        "\n",
        "print(\"Total memory:\", info.total)\n",
        "print(\"Free memory:\", info.free)\n",
        "print(\"Used memory:\", info.used)\n",
        "\n",
        "assert info.free > 1e10, (\n",
        "    \"Looks like your GPU is busy or not having enough 10G memory to continue\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# As of 02/21/2024, we need latest transformer to pick up Gemma tokenizer\n",
        "# Note we might need restart the instance to pick up the changes\n",
        "!pip install -q git+https://github.com/huggingface/transformers > /dev/null\n",
        "!pip install -q bitsandbytes > /dev/null\n",
        "!pip install -q datasets loralib sentencepiece > /dev/null\n",
        "!pip install -q peft > /dev/null"
      ],
      "metadata": {
        "id": "ZaI5k-QZUqV0"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## All the Imports"
      ],
      "metadata": {
        "id": "PClCVhTSUsWj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# disable warnings unless needed\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "xU4z0tNhas13"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset, load_dataset\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import pathlib\n",
        "from peft import PeftModel, get_peft_config, get_peft_model, LoraConfig, TaskType, prepare_model_for_int8_training\n",
        "import pickle\n",
        "import sys\n",
        "import torch\n",
        "import transformers\n",
        "from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig, AutoModelForSeq2SeqLM, DataCollatorForLanguageModeling"
      ],
      "metadata": {
        "id": "JTfioe8DUrro"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define top-level configs"
      ],
      "metadata": {
        "id": "B5UflVhwVCRw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# User: <start_of_turn>user\n",
        "# Knock knock.<end_of_turn>\n",
        "# <start_of_turn>model\n",
        "# Model: Who’s there?<end_of_turn>model\n",
        "# User: <start_of_turn>user\n",
        "# Gemma.<end_of_turn>\n",
        "# <start_of_turn>model\n",
        "# Model: Gemma who?<end_of_turn>model\n"
      ],
      "metadata": {
        "id": "I_7yb9ypmYrl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select your model\n",
        "# MODEL = \"llama-1-7b\"  #@param [\"llama-1-7b\", \"llama-2-7b\", \"llama-2-7b-chat\"]\n",
        "MODEL = \"gg-hf/gemma-7b-it\"\n",
        "model_name_or_path = MODEL\n",
        "tokenizer_name_or_path = MODEL\n",
        "\n",
        "# Max num of tokens (including prompt and output), chinese encoding takes more\n",
        "# than # of chars as observed\n",
        "CUTOFF_LEN = 96\n",
        "# Predict training prompt as well to increase quality as Alpaca Lora does.\n",
        "# Turn off to speedup, but might affect quality.\n",
        "TRAIN_ON_INPUT = True"
      ],
      "metadata": {
        "id": "e0sCGL2FVFsl"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Required to login to get gemma checkpoints\n",
        "!huggingface-cli login"
      ],
      "metadata": {
        "id": "x1O0bVvJ-yN9",
        "outputId": "fd95d5d1-ce3e-4780-8f51-649aad978c77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 7,
      "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) Y\n",
            "Token is valid (permission: read).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "# Somehow Gemma takes way more memory than llama2, why why?\n",
        "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-7b-it\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"google/gemma-7b-it\",\n",
        "                                            #  device_map=\"auto\",\n",
        "                                            #  load_in_8bit=True, # 8bit seems to take more than 9G memory...\n",
        "                                            quantization_config=quantization_config,)\n"
      ],
      "metadata": {
        "id": "gYJtQqOL-dPs",
        "outputId": "f0f24229-1778-4231-e1da-a55f31397ca9",
        "colab": {
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        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.16k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8534783d8f4413f84aaf2aa27617545"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e58c4ffef9744baf95bc92d85f69823a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "43e7b37ea544480eb30eb4d518e57230"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/888 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fbf72740a2d246c38e3b9281c45de85c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/694 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d12bfc5947342baa2c65d85107278ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/20.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1dd1711003144d6d9575a88ae9f4ce27"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00002-of-00004.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70566a30efeb4a079d0210cd369df951"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00003-of-00004.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28b5e5f07b5c4a39b5324cfccb230213"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00004-of-00004.safetensors:   0%|          | 0.00/2.11G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29e9cf4cab2b4280819a042873400ffd"
            }
          },
          "metadata": {}
        },
        {
          "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": "9f77db0ac62940d3b2841a2a23c4a34c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ab131d2246349f79b6da9acb240990f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GEMMA_IT_FORMAT = \"\"\"<start_of_turn>user\n",
        "{user_q}<end_of_turn>\n",
        "<start_of_turn>model\n",
        "\"\"\"\n",
        "\n",
        "print(GEMMA_IT_FORMAT.format(user_q='Hello world?'))\n",
        "tokenizer.encode(GEMMA_IT_FORMAT.format(user_q='Hello world?'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6Ac06Tbpti5",
        "outputId": "084992bd-337c-482f-9ab7-a3e1ec123f9e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<start_of_turn>user\n",
            "Hello world?<end_of_turn>\n",
            "<start_of_turn>model\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 106, 1645, 108, 4521, 2134, 235336, 107, 108, 106, 2516, 108]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out meaning of the Chinese char using ChatGPT\n",
        "def quick_inference(my_model, tokenizer, user_q):\n",
        "  print(user_q)\n",
        "  batch = tokenizer(\n",
        "      GEMMA_IT_FORMAT.format(user_q=user_q),\n",
        "      return_tensors='pt',\n",
        "  )\n",
        "  q_len = batch['input_ids'].shape[1]\n",
        "  with torch.cuda.amp.autocast(): # required for mixed precisions\n",
        "    output_tokens = my_model.generate(\n",
        "        **batch, max_new_tokens=q_len)\n",
        "  out = tokenizer.decode(output_tokens[0][q_len:], skip_special_tokens=False)\n",
        "  print(out)\n",
        "  # print()"
      ],
      "metadata": {
        "id": "tEjxd7ffmx5K"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "quick_inference(model, tokenizer, user_q=\"How does the brain work?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78G97c-urKV3",
        "outputId": "fe744a5b-11d1-4fd7-e9d8-5d3ec9fff569"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The brain is a complex organ that is responsible for controlling all of the functions\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quick_inference(model, tokenizer, user_q=\"对联：闪电\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVblF1_lrK06",
        "outputId": "e5f9c0af-0aad-457d-9bfd-8198ff7330bf"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "闪电在天空舞动，\n",
            "光华洒在山头\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Training data"
      ],
      "metadata": {
        "id": "o_buRbJqV6m_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Reuse my T5 couplet data code https://github.com/hululuzhu/chinese-ai-writing-share/blob/main/training/t5_finetune/Mengzi_T5_Finetune_Chinese_Couplet_V1.ipynb\n",
        "working_dir = \"/tmp/working_dir\"\n",
        "!mkdir -p {working_dir}\n",
        "!wget https://github.com/wb14123/couplet-dataset/releases/download/1.0/couplet.tar.gz -P {working_dir}\n",
        "!ls -l {working_dir}\n",
        "!mkdir -p {working_dir}/couplet_files\n",
        "!tar -xf {working_dir}/couplet.tar.gz -C {working_dir}/couplet_files\n",
        "!head -1 {working_dir}/couplet_files/couplet/train/in.txt {working_dir}/couplet_files/couplet/train/out.txt\n",
        "\n",
        "COUPLET_PATH = f'{working_dir}/couplet_files/couplet'\n",
        "MAX_SEQ_LEN = 32  # Max 32 chinese char including punctuation marks\n",
        "\n",
        "train_df, test_df = None, None\n",
        "for t in ['train', 'test']:\n",
        "  ins, outs = [], []\n",
        "  for i in ['in', 'out']:\n",
        "    with open(f\"{COUPLET_PATH}/{t}/{i}.txt\", \"r\") as f:\n",
        "      for line in f:\n",
        "        clean_line = line.strip().replace(' ', '').replace('\\n', '').replace('\\r', '')[:MAX_SEQ_LEN]\n",
        "        if i=='in':\n",
        "          ins.append(clean_line)\n",
        "        else:\n",
        "          outs.append(clean_line)\n",
        "  # The column names to match simpleT5\n",
        "  data_dict = {\n",
        "      'source_text': ins,\n",
        "      'target_text': outs,\n",
        "  }\n",
        "  if t == 'train':\n",
        "    train_df = pd.DataFrame(data_dict)\n",
        "  else:\n",
        "    test_df = pd.DataFrame(data_dict)\n",
        "\n",
        "def get_gemma_prompts(df):\n",
        "  raw_prompts = df['source_text'].tolist()\n",
        "  gemma_prompts = []\n",
        "  for p in raw_prompts:\n",
        "    gemma_prompts.append(GEMMA_IT_FORMAT.format(user_q=p))\n",
        "  return gemma_prompts\n",
        "\n",
        "train_df['source_text'] = get_gemma_prompts(train_df)\n",
        "test_df['source_text'] = get_gemma_prompts(test_df)\n",
        "\n",
        "# COUPLET_PROMPOT = '对联：'\n",
        "# COUPLET_SUFFIX = '\\n下联：'\n",
        "# train_df['source_text'] = COUPLET_PROMPOT + train_df['source_text'] + COUPLET_SUFFIX\n",
        "# test_df['source_text'] = COUPLET_PROMPOT + test_df['source_text'] + COUPLET_SUFFIX"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xsi0FxcGV8PF",
        "outputId": "8726c32c-e9a4-46e2-8612-694506708342"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-02 19:43:09--  https://github.com/wb14123/couplet-dataset/releases/download/1.0/couplet.tar.gz\n",
            "Resolving github.com (github.com)... 140.82.113.4\n",
            "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/122695108/9643dda6-194e-11e8-9642-44c7d57d40ac?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240302%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240302T194310Z&X-Amz-Expires=300&X-Amz-Signature=de183986510e09dd06cf3798bbfce4a8ce432f4c24096505ee3228cbfb121726&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=122695108&response-content-disposition=attachment%3B%20filename%3Dcouplet.tar.gz&response-content-type=application%2Foctet-stream [following]\n",
            "--2024-03-02 19:43:10--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/122695108/9643dda6-194e-11e8-9642-44c7d57d40ac?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240302%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240302T194310Z&X-Amz-Expires=300&X-Amz-Signature=de183986510e09dd06cf3798bbfce4a8ce432f4c24096505ee3228cbfb121726&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=122695108&response-content-disposition=attachment%3B%20filename%3Dcouplet.tar.gz&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 27412598 (26M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/working_dir/couplet.tar.gz.1’\n",
            "\n",
            "couplet.tar.gz.1    100%[===================>]  26.14M   143MB/s    in 0.2s    \n",
            "\n",
            "2024-03-02 19:43:10 (143 MB/s) - ‘/tmp/working_dir/couplet.tar.gz.1’ saved [27412598/27412598]\n",
            "\n",
            "total 53548\n",
            "drwxr-xr-x 3 root root     4096 Mar  2 19:42 couplet_files\n",
            "-rw-r--r-- 1 root root 27412598 Dec  6  2021 couplet.tar.gz\n",
            "-rw-r--r-- 1 root root 27412598 Dec  6  2021 couplet.tar.gz.1\n",
            "==> /tmp/working_dir/couplet_files/couplet/train/in.txt <==\n",
            "晚 风 摇 树 树 还 挺 \n",
            "\n",
            "==> /tmp/working_dir/couplet_files/couplet/train/out.txt <==\n",
            "晨 露 润 花 花 更 红 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['target_text'] += '<eos>'\n",
        "test_df['target_text'] += '<eos>'"
      ],
      "metadata": {
        "id": "MZTN-VT4t7Xv"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample 5k\n",
        "train_df_sample = train_df[['source_text', 'target_text']].sample(5000)\n",
        "train_df_sample.sample(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "vFVQGl_AWQf-",
        "outputId": "37d28a4e-48e1-47f3-c11a-409d1b59eea8"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              source_text  \\\n",
              "531154  <start_of_turn>user\\n君子之交如水淡<end_of_turn>\\n<st...   \n",
              "520618  <start_of_turn>user\\n殚心力以报所知，一代长才出甘陇<end_of_tu...   \n",
              "644218  <start_of_turn>user\\n落花声里春成冢<end_of_turn>\\n<st...   \n",
              "\n",
              "                 target_text  \n",
              "531154          鸿鹄所向与天高<eos>  \n",
              "520618  处腊膏而不自润，千秋遗爱满邗江<eos>  \n",
              "644218          离别梦中泪满襟<eos>  "
            ],
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <td>&lt;start_of_turn&gt;user\\n殚心力以报所知，一代长才出甘陇&lt;end_of_tu...</td>\n",
              "      <td>处腊膏而不自润，千秋遗爱满邗江&lt;eos&gt;</td>\n",
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              "      <td>&lt;start_of_turn&gt;user\\n落花声里春成冢&lt;end_of_turn&gt;\\n&lt;st...</td>\n",
              "      <td>离别梦中泪满襟&lt;eos&gt;</td>\n",
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              "                                                    [key], {});\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"train_df_sample\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"source_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"<start_of_turn>user\\n\\u541b\\u5b50\\u4e4b\\u4ea4\\u5982\\u6c34\\u6de1<end_of_turn>\\n<start_of_turn>model\\n\",\n          \"<start_of_turn>user\\n\\u6b9a\\u5fc3\\u529b\\u4ee5\\u62a5\\u6240\\u77e5\\uff0c\\u4e00\\u4ee3\\u957f\\u624d\\u51fa\\u7518\\u9647<end_of_turn>\\n<start_of_turn>model\\n\",\n          \"<start_of_turn>user\\n\\u843d\\u82b1\\u58f0\\u91cc\\u6625\\u6210\\u51a2<end_of_turn>\\n<start_of_turn>model\\n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u9e3f\\u9e44\\u6240\\u5411\\u4e0e\\u5929\\u9ad8<eos>\",\n          \"\\u5904\\u814a\\u818f\\u800c\\u4e0d\\u81ea\\u6da6\\uff0c\\u5343\\u79cb\\u9057\\u7231\\u6ee1\\u9097\\u6c5f<eos>\",\n          \"\\u79bb\\u522b\\u68a6\\u4e2d\\u6cea\\u6ee1\\u895f<eos>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Convert Data to Training-friendly DataSet"
      ],
      "metadata": {
        "id": "6b8QLu8UXafg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Copied from Alpaca-LoRA, notice input_ids, attention_mask, and labels are\n",
        "# default expected columns in huggingface dataset lib\n",
        "def tokenize(tokenizer, prompt, cutoff_len, add_eos_token=False):\n",
        "  # there's probably a way to do this with the tokenizer settings\n",
        "  # but again, gotta move fast\n",
        "  result = tokenizer(\n",
        "      prompt,\n",
        "      truncation=True,\n",
        "      max_length=cutoff_len,\n",
        "      padding=False,\n",
        "      return_tensors=None,\n",
        "  )\n",
        "  if (\n",
        "      result[\"input_ids\"][-1] != tokenizer.eos_token_id\n",
        "      and len(result[\"input_ids\"]) < cutoff_len\n",
        "      and add_eos_token\n",
        "  ):\n",
        "    result[\"input_ids\"].append(tokenizer.eos_token_id)\n",
        "    result[\"attention_mask\"].append(1)\n",
        "\n",
        "  # result[\"labels\"] = copy.deepcopy(result[\"input_ids\"])\n",
        "  result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "  return result\n",
        "\n",
        "\n",
        "# Branched from Alpaca-LoRA\n",
        "def tokenize_fn(data_point):\n",
        "  prompt_in, prompt_out = data_point['source_text'], data_point['target_text']\n",
        "  full_prompt = prompt_in + prompt_out\n",
        "  tokenized_full_prompt = tokenize(tokenizer, full_prompt, CUTOFF_LEN)\n",
        "  if not TRAIN_ON_INPUT:\n",
        "    user_prompt = prompt_in\n",
        "    tokenized_user_prompt = tokenize(tokenizer, user_prompt, CUTOFF_LEN, add_eos_token=False)\n",
        "    user_prompt_len = len(tokenized_user_prompt[\"input_ids\"])\n",
        "    tokenized_full_prompt[\"labels\"] = [\n",
        "        -100 # special id for skipping\n",
        "    ] * user_prompt_len + tokenized_full_prompt[\"labels\"][user_prompt_len:]\n",
        "  return tokenized_full_prompt\n",
        "\n",
        "\n",
        "train_ds = Dataset.from_pandas(train_df_sample)\n",
        "train_ds = train_ds.flatten()\n",
        "tokenized_train_ds = train_ds.map(\n",
        "    tokenize_fn,\n",
        "    remove_columns=['source_text', 'target_text', '__index_level_0__'],\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "a6e7cd7c9f9740a3a888b96525a4c0da",
            "b1c8ca09c93140b3a7b31ab3a14a75dd",
            "658039dbc5894730b6b35b14d93ab45e",
            "c631e1d77e1a45dbbdfeab7ef74f5830",
            "ef720faed3c54f21ad185831e7b32411",
            "2f802e9eaef34c61915d3de472950c83",
            "498f854488e745029c4013f0e76f1960",
            "a846988312824f33b52d5fb90cf979d4",
            "b334d9cc1fa849b892672017c77a9303",
            "7cff57d2d27e4c9aaa37a85604c77bbc",
            "40b6564e7b4440eda1b4f300b4eaa951"
          ]
        },
        "id": "bi6C8fsIWVkM",
        "outputId": "c3971c47-2ece-4299-c96a-c595c2ddff14"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6e7cd7c9f9740a3a888b96525a4c0da"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Optionally check a few examples by decoding the inputs\n",
        "for i in range(100, 103):\n",
        "  print(\"token length\", len(tokenized_train_ds['input_ids'][i]))\n",
        "  print(tokenizer.decode(tokenized_train_ds['input_ids'][i]))\n",
        "  print(\"Label ids\", tokenized_train_ds['labels'][i])\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jq129F2LXlRV",
        "outputId": "47836396-352f-48bb-a94c-7f57e70a1036"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "token length 35\n",
            "<bos><start_of_turn>user\n",
            "书法、美术、诗词，廉信古今秀<end_of_turn>\n",
            "<start_of_turn>model\n",
            "园林、馆舍、亭台，景观方圆稀<eos>\n",
            "Label ids [2, 106, 1645, 108, 197290, 235394, 150823, 235394, 238143, 237379, 235365, 239751, 235851, 236194, 235811, 237213, 107, 108, 106, 2516, 108, 237156, 236234, 235394, 237463, 238564, 235394, 238806, 235945, 235365, 189844, 235576, 237869, 239396, 1]\n",
            "\n",
            "token length 22\n",
            "<bos><start_of_turn>user\n",
            "燕栖柳绿三分暖<end_of_turn>\n",
            "<start_of_turn>model\n",
            "蝶弄桃红一段新<eos>\n",
            "Label ids [2, 106, 1645, 108, 238596, 241247, 238110, 237631, 144157, 237649, 107, 108, 106, 2516, 108, 238807, 238613, 237692, 236479, 64438, 235630, 1]\n",
            "\n",
            "token length 34\n",
            "<bos><start_of_turn>user\n",
            "苦累为消防，练成绝技能擒虎<end_of_turn>\n",
            "<start_of_turn>model\n",
            "光荣因奉献，捧出忠心可感天<eos>\n",
            "Label ids [2, 106, 1645, 108, 236954, 238236, 235640, 99096, 235365, 237535, 235636, 237127, 41846, 242888, 237655, 107, 108, 106, 2516, 108, 235973, 238203, 235933, 238745, 237225, 235365, 240158, 235531, 238284, 235675, 235553, 235746, 235654, 1]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LoRA setup\n",
        "- Check out LoRA paper\n",
        "- Why q_proj and v_proj?\n",
        "  - ![](https://lh3.googleusercontent.com/pG1o98-ZuTdvGaIuf3r0_GQ2wqZv1eAjaM13ki_AoipSm4Vo0v3JCynmU26PjE_6qKvyLdiDlZfQP8mGpvy0hG6TMDM-ROpup35WYmH3lBiGVC67tQL2kZnNIVzz0gviU88lq7yP126N1DCaKxkvXd1vE5TBwasBTH2waI_QbcyT324snp5iOCJXDrMa9bPokbM4w8PwJL61lqfGXmOvbP2Yqo5gOC7kA73aZPMOG3CnWzFujZpbQ5so7ZnHNOvmBWSjKUHvqI8UbvJvAy43SXL2UePcFg-KcWAA9gCUscNxKOOtai0_6ShZgZLCXCMLvLYpbqK6IqtYTS7-dwQMYQrRJ80IyPxMYwfSLaYn2UVd0I04ETFCqOH-pDtsToZ3eCGqQi-zxLdcDUpqhcXxj60PjxpMOyFK_wCK1tKEx7hu7nUDR4GYIIRFtNZS_jMhFaKhqcZf6d3Vora-2v0Sv_CVhDTy5cabVpSDEqBWpGMiCcj5IvnBIRAkPY5D_Mr5elWSCuanOXMp9riwK2-WobJoNvW7qATFAr3aiTA5MCQPqwvkOXhpj9YF7QudshxaplDzpBiLxJbdvzE-froAlxAup2yDEhEOb_xuvRBLetvL366GOEivlq577Y8MTusVcz_b9ex6TP77_XjRHAp4lQ7Bs7tR2tjY-n29bC1MhGB_t7Ta82MdLivR-T5lG4hvhGJ-rTsqMkUm0KY-Vqup-04eZHBMkY1RHjj7oNc8vDXHbTiFskLfne5Trr0_3MCZamyRZuwPeZXzFlzbif1lSBwXpSk0ckzPMGRFhiDZ0sa3QUrLeyvGA5UzHIhqHL0Ve-f03V0z48o_YoHSdWrhN8xZJb6ga-eGu0MM9f5VxE7Y9znQ4qE9_5neS6GBHvA0-YXjzZ7INP9KVgKpX_FTuAuegL7ARB1gG4lbXKWVKQS38g=w1577-h337-s-no?authuser=0)\n",
        "- why r=16 (>=8)\n",
        "  - ![](https://lh3.googleusercontent.com/sxLGQpoBbmjnZwK853wFOcgEgzvJIa7wOpaH72v1eNw9gI9VaMvhpWGhzPCPowSuG44wzO53ENrXGMrdoXXhTjPmy1jRVvAMqbFYiwcCU4sZ0jqOe2vP1I9hEw-syKqpPW1-Nr5TM10Qm8MYXuigatFPNl76FSxYXBRHNcZRjeluGPxMjz78SXzBa07j6YomCGQJCyx5QTRVfhWw7iy4dbb1rybldeodUvY1xI8XzTzQeclYhE8kLI6yN7J02LKpkhHmzMgFY0Qr73gvoINGmZyguJItJ0ZcaR3zBJNfIIaBSaYe3amB4qL7zWu6sYOxfdBk8v_lWLCCTys1_ThFoiUhLDrHRK1LX5QELQTf_MAlFVk5qisF2dZo3GFEt5bKga2CNwSH-I5FjL9Z6jnopRDHCs1JGVmYn6sgdLhrG7fbj83hAb5NLwSfebi5pjYRASAVVC18hNo1ZkG_TCTPJv2__KveWNjalDkSWWEVzMZO6ZlnoLMtwEA_KfqaDNEdVTs2wa_-dsbXijPDkF0bSdiDqtiAe6Nk_sL1iEoNMswMvCGOD5orD4oojigkteh-xdyaQ3W0mEqC3HXEaPAAKHQOp2V5XBIMi-wIo_M-bjK304SA68jvmizpyYTI-yTiAb_B8lUR0PeAMp1avZux4NdXVZZu1wWlzgpr3HC7pU7ZmqgO_xJrU-ICMrkeqy8eYOy-jy1NrK03Y9NsT7-JTxg5HHGBptMKkJORT7IIQRl_eCgw0WMu9Bc9ueSIbSCLQgZ_WdaMe3wSjLkj8NmRgQ83HW686Ww54xfwscMq8l97MgaJobKqRvagOzx2KG_cMthGWfkIqVFCdfTgBv3c8Mf7lBDwduxsyfNbLuPSxW_NI4UmxN7Tkx5xN_qBBI5prltAYX_jYhFtq6JNd2bnWgqDBfDDkHp96Wj3kMRF43A8sw=w1804-h507-s-no?authuser=0)"
      ],
      "metadata": {
        "id": "_wcnKGeNXwCo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = prepare_model_for_int8_training(model)\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32, # scaling param related to r, reuse alpaca-lora\n",
        "    target_modules=[\"q_proj\", \"v_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "lora_model = get_peft_model(model, config)"
      ],
      "metadata": {
        "id": "TTul-gBRXvVY"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training\n",
        "- Show before and after!"
      ],
      "metadata": {
        "id": "5T1SU9JkYxmf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "quick_inference(lora_model, tokenizer, user_q=\"How does the brain work?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gl5a91-AtBwg",
        "outputId": "ea72c97f-9f66-42bb-81fd-2b23b243f52c"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "How does the brain work?\n",
            "The brain is a complex organ that is responsible for controlling all of the functions\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out meaning of the Chinese char using ChatGPT\n",
        "def eval_model(my_model, examples=[\"春风得意花铺路\",\n",
        "                                   \"美丽中国魅力北京\",\n",
        "                                   \"鱼书千里梦\",\n",
        "                                   \"日落晚霞临古寺\",]):\n",
        "  for p_in in examples:\n",
        "    quick_inference(my_model, tokenizer, p_in)"
      ],
      "metadata": {
        "id": "wxCMYMcF4IRw"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Different GPUs may give out slightly different answers below due to very small precision difference\n",
        "print(\"Before training\")\n",
        "eval_model(lora_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rMfEwhTfY0NC",
        "outputId": "2e6f0348-9ee9-4822-da14-d16f65f19041"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before training\n",
            "春风得意花铺路\n",
            "春风得意花铺路，春暖花开，美不胜\n",
            "美丽中国魅力北京\n",
            "**美丽中国魅力北京，令人惊叹的现代与传统\n",
            "鱼书千里梦\n",
            "鱼书千里梦，是一个关于爱慕和成长的小说\n",
            "日落晚霞临古寺\n",
            "夕暮暮色，日落缓缓西下，将天空渲染成 Varies\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "LZmFMCf8Dfkq"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# As you can tell, I even omitted eval_dataset for this demo :(\n",
        "trainer = transformers.Trainer(\n",
        "    model=lora_model,\n",
        "    train_dataset=tokenized_train_ds,\n",
        "    args=transformers.TrainingArguments(\n",
        "        # increased batch size will significantly increase GPU requirement here\n",
        "        # Decrease to 4 if you have less than 16G vram\n",
        "        # Batch = 4, probably 8.3-8.8G vram\n",
        "        # Batch = 16, 9.5G+\n",
        "        # Batch = 32, 11G+\n",
        "        # Batch = 64, 14G+\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=2,\n",
        "        warmup_steps=8,\n",
        "        num_train_epochs=2,\n",
        "        learning_rate=2e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=20,\n",
        "        output_dir='outputs',\n",
        "        remove_unused_columns=False,\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForSeq2Seq(\n",
        "        tokenizer, pad_to_multiple_of=8, return_tensors=\"pt\", padding=True,\n",
        "    ),\n",
        ")\n",
        "lora_model.config.use_cache = False # Alpaca Lora sets this for training\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "yb7K0PsYY6T3",
        "outputId": "f3a9479f-8248-4846-f0da-e4d893dfa98f"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='161' max='1250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 161/1250 10:07 < 1:09:22, 0.26 it/s, Epoch 0.26/2]\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>20</td>\n",
              "      <td>33.509300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>14.764900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>6.039900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>5.201800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>4.914200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>4.787700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>4.738000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-88-666ee40285ea>\u001b[0m in \u001b[0;36m<cell line: 27>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m )\n\u001b[1;32m     26\u001b[0m \u001b[0mlora_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;31m# Alpaca Lora sets this for training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mtrainer\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[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   1624\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   1625\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-> 1626\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1627\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   1628\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   1961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1962\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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-> 1963\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1964\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1965\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)\u001b[0m\n\u001b[1;32m   2902\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2903\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-> 2904\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2905\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2906\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_gpu\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;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m   2925\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   2926\u001b[0m             \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2927\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\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   2928\u001b[0m         \u001b[0;31m# Save past state if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2929\u001b[0m         \u001b[0;31m# TODO: this needs to be fixed and made cleaner later.\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\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-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\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/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    815\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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--> 817\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    819\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\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/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    803\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    804\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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--> 805\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     15\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"@autocast() decorator is not supported in script mode\"\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\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/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, task_ids, **kwargs)\u001b[0m\n\u001b[1;32m   1089\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpeft_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpeft_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mPeftType\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPOLY\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1090\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"task_ids\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtask_ids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1091\u001b[0;31m             return self.base_model(\n\u001b[0m\u001b[1;32m   1092\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1093\u001b[0m                 \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/tuners/tuners_utils.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\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--> 160\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mabstractmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/gemma/modeling_gemma.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, cache_position)\u001b[0m\n\u001b[1;32m   1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1081\u001b[0m         \u001b[0;31m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1082\u001b[0;31m         outputs = self.model(\n\u001b[0m\u001b[1;32m   1083\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1084\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\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/accelerate/hooks.py\u001b[0m in \u001b[0;36mnew_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\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--> 166\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    167\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/gemma/modeling_gemma.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, cache_position)\u001b[0m\n\u001b[1;32m    902\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient_checkpointing\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 904\u001b[0;31m                 layer_outputs = self._gradient_checkpointing_func(\n\u001b[0m\u001b[1;32m    905\u001b[0m                     \u001b[0mdecoder_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m                     \u001b[0mhidden_states\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/torch/_compile.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     22\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dynamo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dynamo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursive\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner\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/torch/_dynamo/eval_frame.py\u001b[0m in \u001b[0;36m_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0mdynamic_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__enter__\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    327\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    329\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 \u001b[0mset_eval_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprior\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/torch/_dynamo/external_utils.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\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     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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---> 17\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner\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/torch/utils/checkpoint.py\u001b[0m in \u001b[0;36mcheckpoint\u001b[0;34m(function, use_reentrant, context_fn, determinism_check, debug, *args, **kwargs)\u001b[0m\n\u001b[1;32m    449\u001b[0m                 \u001b[0;34m\"use_reentrant=False.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m             )\n\u001b[0;32m--> 451\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mCheckpointFunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreserve\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m    452\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    453\u001b[0m         gen = _checkpoint_without_reentrant_generator(\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/gemma/modeling_gemma.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, cache_position, **kwargs)\u001b[0m\n\u001b[1;32m    645\u001b[0m         \u001b[0mresidual\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    646\u001b[0m         \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_attention_layernorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 647\u001b[0;31m         \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\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    648\u001b[0m         \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresidual\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/gemma/modeling_gemma.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\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--> 175\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdown_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgate_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mup_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\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-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\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/accelerate/hooks.py\u001b[0m in \u001b[0;36mnew_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\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--> 166\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    167\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0mbias\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul_4bit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquant_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquant_state\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    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp_dtype\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/bitsandbytes/autograd/_functions.py\u001b[0m in \u001b[0;36mmatmul_4bit\u001b[0;34m(A, B, quant_state, out, bias)\u001b[0m\n\u001b[1;32m    575\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\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--> 577\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatMul4Bit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquant_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m    537\u001b[0m             \u001b[0;31m# See NOTE: [functorch vjp and autograd interaction]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m             \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_functorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap_dead_wrappers\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[0;32m--> 539\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup_context\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_SingleLevelFunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup_context\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/bitsandbytes/autograd/_functions.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(ctx, A, B, out, bias, quant_state)\u001b[0m\n\u001b[1;32m    514\u001b[0m         \u001b[0;31m# 1. Dequantize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m         \u001b[0;31m# 2. MatmulnN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 516\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdequantize_4bit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquant_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\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    517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    518\u001b[0m         \u001b[0;31m# 3. Save state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Empirical quick tests showed \"somehow ok\" results if loss < THRESHOLD\n",
        "print(\"After training\")\n",
        "eval_model(lora_model)"
      ],
      "metadata": {
        "id": "xilHS3n9ZKj7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "128a094f-1629-475e-c000-a4f0e7d13e95"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After training\n",
            "春风得意花铺路\n",
            "秋雨流派诗作韵<eos>\n",
            "美丽中国魅力北京\n",
            "和谐社会和谐社会<eos>\n",
            "鱼书千里梦\n",
            "鸟歌百载歌<eos>\n",
            "日落晚霞临古寺\n",
            "月明清风送客船<eos>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Suggested additional reading\n",
        "- [Decoding algorithm by HF](https://huggingface.co/blog/how-to-generate)\n",
        "- So far, I only demoed greedy search (output token with highest prob at each position without looking ahead)"
      ],
      "metadata": {
        "id": "p8CQ4NrklkLm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Optional: Upload to HuggingFace and share with the world!\n",
        "- And you should!"
      ],
      "metadata": {
        "id": "c1qHzgL9ZPU6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from huggingface_hub import notebook_login\n",
        "# notebook_login()\n",
        "# YOUR_HF_ID = \"hululuzhu\"\n",
        "# lora_model.push_to_hub(f\"{YOUR_HF_ID}/chinese-couplet-gemma-lora-test-v0.1\",\n",
        "#                        use_auth_token=True,\n",
        "#                        create_pr=True)\n",
        "# Go to huggingface and merge the PR to share with the world!"
      ],
      "metadata": {
        "id": "Zept7b_5ZUbX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314,
          "referenced_widgets": [
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        "outputId": "1393c146-7d8a-4d11-e433-220ce0d9c0fe"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/752 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30443985f08c4eea850a958fed726a5a"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/25.7M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/hululuzhu/chinese-couplet-gemma-lora-test-v0.1/commit/5707739eed53dacba7925baf9ce196b3ef1c0954', commit_message='Upload model', commit_description='', oid='5707739eed53dacba7925baf9ce196b3ef1c0954', pr_url='https://huggingface.co/hululuzhu/chinese-couplet-gemma-lora-test-v0.1/discussions/2', pr_revision='refs/pr/2', pr_num=2)"
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          "metadata": {},
          "execution_count": 90
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}