fine_tune_code_llama.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5rfduRD1CSF"
      },
      "source": [
        "# A guide to fine-tuning Code Llama\n",
        "\n",
        "**In this guide I show you how to fine-tune Code Llama to become a beast of an SQL developer. For coding tasks, you can generally get much better performance out of Code Llama than Llama 2, especially when you specialise the model on a particular task:**\n",
        "\n",
        "- I use the [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) which is a bunch of text queries and their corresponding SQL queries\n",
        "- A Lora approach, quantizing the base model to int 8, freezing its weights and only training an adapter\n",
        "- Much of the code is borrowed from [alpaca-lora](https://github.com/tloen/alpaca-lora), but I refactored it quite a bit for this\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HFQ9XeBI1CSG"
      },
      "source": [
        "### 2. Pip installs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0x4OWfq1CSG",
        "outputId": "85954959-c527-4e20-f91b-1725a596ba31"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers.git@main\n",
            "  Cloning https://github.com/huggingface/transformers.git (to revision main) to /tmp/pip-req-build-dsrdwty_\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-dsrdwty_\n",
            "  Resolved https://github.com/huggingface/transformers.git to commit e65502951593a76844e872fee9c56b805598538a\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting bitsandbytes\n",
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            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting git+https://github.com/huggingface/peft.git@e536616888d51b453ed354a6f1e243fecb02ea08\n",
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            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-gxd2ld0j\n",
            "  Running command git rev-parse -q --verify 'sha^e536616888d51b453ed354a6f1e243fecb02ea08'\n",
            "  Running command git fetch -q https://github.com/huggingface/peft.git e536616888d51b453ed354a6f1e243fecb02ea08\n",
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            "Installing collected packages: xxhash, dill, responses, multiprocess, datasets\n",
            "Successfully installed datasets-2.10.1 dill-0.3.6 multiprocess-0.70.14 responses-0.18.0 xxhash-3.4.1\n",
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            "\u001b[?25hCollecting setproctitle (from wandb)\n",
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            "  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: smmap, setproctitle, sentry-sdk, docker-pycreds, gitdb, gitpython, wandb\n",
            "Successfully installed docker-pycreds-0.4.0 gitdb-4.0.11 gitpython-3.1.43 sentry-sdk-2.7.1 setproctitle-1.3.3 smmap-5.0.1 wandb-0.17.3\n",
            "Collecting git+https://github.com/huggingface/accelerate\n",
            "  Cloning https://github.com/huggingface/accelerate to /tmp/pip-req-build-ybbq40u5\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/accelerate /tmp/pip-req-build-ybbq40u5\n",
            "  Resolved https://github.com/huggingface/accelerate to commit c799c198e9d8096e3fffeb962b3adf34e5397d5e\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (12.1.105)\n",
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            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (2.20.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (12.1.105)\n",
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            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate==0.32.0.dev0) (12.5.40)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate==0.32.0.dev0) (2.0.7)\n",
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            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate==0.32.0.dev0) (1.3.0)\n",
            "Building wheels for collected packages: accelerate\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for accelerate: filename=accelerate-0.32.0.dev0-py3-none-any.whl size=311915 sha256=d60c1b8c937d4c9992b76184d33f8626990edd6ae910b2025ddf25e180cf6fb0\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-4f_elh6m/wheels/f6/c7/9d/1b8a5ca8353d9307733bc719107acb67acdc95063bba749f26\n",
            "Successfully built accelerate\n",
            "Installing collected packages: accelerate\n",
            "  Attempting uninstall: accelerate\n",
            "    Found existing installation: accelerate 0.20.3\n",
            "    Uninstalling accelerate-0.20.3:\n",
            "      Successfully uninstalled accelerate-0.20.3\n",
            "Successfully installed accelerate-0.32.0.dev0\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/huggingface/transformers.git@main bitsandbytes accelerate==0.20.3  # we need latest transformers for this\n",
        "!pip install git+https://github.com/huggingface/peft.git@e536616888d51b453ed354a6f1e243fecb02ea08\n",
        "!pip install datasets==2.10.1\n",
        "!pip install wandb\n",
        "!pip install git+https://github.com/huggingface/accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mCmGzYg51CSH"
      },
      "source": [
        "I used an A100 GPU machine with Python 3.10 and cuda 11.8 to run this notebook. It took about an hour to run."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mu9JczX1CSH"
      },
      "source": [
        "### Loading libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import locale # colab workaround (MS - why?)\n",
        "#locale.getpreferredencoding = lambda _: \"UTF-8\" # colab workaround (MS - why?)"
      ],
      "metadata": {
        "id": "mploAwenyY1b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oTeYW8z51CSH"
      },
      "outputs": [],
      "source": [
        "from datetime import datetime\n",
        "import os\n",
        "import sys\n",
        "\n",
        "import torch\n",
        "from peft import (\n",
        "    LoraConfig,\n",
        "    get_peft_model,\n",
        "    get_peft_model_state_dict,\n",
        "    prepare_model_for_int8_training,\n",
        "    set_peft_model_state_dict,\n",
        ")\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForSeq2Seq\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32zH9-hM1CSH"
      },
      "source": [
        "(If you have import errors, try restarting your Jupyter kernel)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4M9KyT0S1CSH"
      },
      "source": [
        "### Load dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308,
          "referenced_widgets": [
            "c31be5d602e64c5cac38693d601acff7",
            "0ec9f66617644ef98e22bd8eebaa1b19",
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            "371469506e5243ac967085e68af8a82e",
            "72df7d9380fd4c14b66d4c13e6c57a20",
            "4a7fd75012f5444b98cadecded25ed70",
            "3761062b59b04588bdb4ee4c8fe04ba7",
            "bbca0c1dae6e4cf984392075200a34d3",
            "3dc10ba82f8948ea92144f4a54f418f4",
            "c84c0cd440c84ec98f773a55180fdab2",
            "8edafecdeefd41329f9642c600ff7428",
            "8bb3a968a4b84f3c86328ce49395f90a",
            "cdef8836c4284d1b98dbb07e65c24ec3",
            "1c0077241deb4e77b8b152d6a2e2bb4c",
            "9e410cb390494c92b883ed7a2cc12adf",
            "5033c87a47504cc7b3f37257f077e021",
            "691a710a120b411f8ba2fa39046989ae",
            "e68a1ae39fe044028006a10cb74918db",
            "c26c9b5ac0194106af9bad10acb141b9",
            "4ddaf4df640548e0ab6a92281e7639ab",
            "17e20ebd1a674149bc2b636434971886",
            "a17c1fab4d444206ac36ee0ab34c68a7",
            "9ecd37ddb6f04483a7298015d840cd73",
            "298835f687b4471486d11711b857876c",
            "bfe35c4fef9442a5a3f788d930376ea6",
            "140a95c743e9405da23de4ca012d647b",
            "560485aef18d418ebafe2f4043762535",
            "2b5ef4d827a5430691d98c035bae4596",
            "d21b0bc311364ccea13650e956b75295",
            "0e483d8b7a064f919090d89248f21d76",
            "128472f2459a4d6eb4aa920bd78f4abc",
            "8c47ee45130a454f90e2d0d59b07acfc",
            "a8f0bd6483694aefac9a18e99f839ce2",
            "84ea78fb849f4558b272df0f2693bb0e",
            "ad15d91e8e884020a18d023696ed0897",
            "3937159009bc4c7aad2a7551a35a591d",
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            "31a0b03f535a4744911f21f1e7cfd703",
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            "bdf0beca44944d078b0d86ddb4b90a34",
            "7f31603cfba34008a151574f054d2c27",
            "4047ec990aef4ea585728bb00590b665",
            "71720a930cb64fdb86fc7f675d7fe86e",
            "a6046df162534dd3bda90a2985346925",
            "e97366424f8e42d6acb597f3e09582a1",
            "113f6b41b96e419c842f499b797d7790",
            "4e46bba497f444a7bfd799b6cd2b9c33",
            "6b2fbf4b32624b6cbbd5b5d033a92b04",
            "64b21f0a68fc43a6aee4e7905e488a0a"
          ]
        },
        "id": "w44O1EK-1CSH",
        "outputId": "a3196ea0-b919-4717-f364-ead1ee039eb3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/4.43k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c31be5d602e64c5cac38693d601acff7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/b-mc2--sql-create-context to /root/.cache/huggingface/datasets/b-mc2___json/b-mc2--sql-create-context-b6e5c5a03fb67709/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8edafecdeefd41329f9642c600ff7428"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/21.8M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a17c1fab4d444206ac36ee0ab34c68a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a8f0bd6483694aefac9a18e99f839ce2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d29b558467f4ba78fd0e7f5bc32ad71"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/b-mc2___json/b-mc2--sql-create-context-b6e5c5a03fb67709/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"b-mc2/sql-create-context\", split=\"train\")\n",
        "train_dataset = dataset.train_test_split(test_size=0.1)[\"train\"]\n",
        "eval_dataset = dataset.train_test_split(test_size=0.1)[\"test\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJ54EffO1CSI"
      },
      "source": [
        "The above pulls the dataset from the Huggingface Hub and splits 10% of it into an evaluation set to check how well the model is doing through training. If you want to load your own dataset do this:\n",
        "\n",
        "```\n",
        "train_dataset = load_dataset('json', data_files='train_set.jsonl', split='train')\n",
        "eval_dataset = load_dataset('json', data_files='validation_set.jsonl', split='train')\n",
        "```\n",
        "\n",
        "And if you want to view any samples in the dataset just do something like:``` ```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFbeaZzf1CSJ",
        "outputId": "25fc024b-2eed-4943-86c0-48542e878254"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'question': 'What region has giuseppe sivori as a home venue?', 'context': 'CREATE TABLE table_name_52 (region VARCHAR, home_venue VARCHAR)', 'answer': 'SELECT region FROM table_name_52 WHERE home_venue = \"giuseppe sivori\"'}\n"
          ]
        }
      ],
      "source": [
        "print(train_dataset[3])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHdMYcu61CSJ"
      },
      "source": [
        "Each entry is made up of a text 'question', the sql table 'context' and the 'answer'."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ig7NvWN1CSJ"
      },
      "source": [
        "### Load model\n",
        "I load code llama from huggingface in int8. Standard for Lora:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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          ]
        },
        "id": "rMnU93bY1CSJ",
        "outputId": "384bbb16-f429-4885-dca4-77b0f0e38355"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/637 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eab2a932d0794c46aaf4f94a9822d5b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/25.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf4963f7d1d745a99869377020edd145"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7cf0c39f0a844b91be86a732a025e50d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee72e9996bc34b2c9b1d003a8c9ce943"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7216323c76314bb6a6d227e94f606959"
            }
          },
          "metadata": {}
        },
        {
          "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": "c2f340f3e49f4a06a86d196b3c0c5cee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6470582c0fd4654ab4211d216709342"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/749 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a222c7ba86df4ef0b22c929cdb9a02b7"
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          },
          "metadata": {}
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          "output_type": "display_data",
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          "metadata": {}
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          "output_type": "display_data",
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            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "base_model = \"codellama/CodeLlama-7b-hf\"\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model,\n",
        "    load_in_8bit=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"codellama/CodeLlama-7b-hf\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3OF-wtj1CSJ"
      },
      "source": [
        "torch_dtype=torch.float16 means computations are performed using a float16 representation, even though the values themselves are 8 bit ints.\n",
        "\n",
        "\n",
        "If you get error \"ValueError: Tokenizer class CodeLlamaTokenizer does not exist or is not currently imported.\" Make sure you have transformers version is 4.33.0.dev0 and accelerate is >=0.20.3.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2VXqJJe1CSJ"
      },
      "source": [
        "### 3. Check base model\n",
        "A very good common practice is to check whether a model can already do the task at hand. Fine-tuning is something you want to try to avoid at all cost:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uiyAff1a1CSJ",
        "outputId": "f4813f3b-8398-42b4-ecfd-e52267cdc4c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a powerful purescript-to-javascript compiler. Your job is to convert purescript code into javascript. You are given \"Input\" in purescript, \"Context PS\" showing a similar chunk of PureScript and \"Context JS\" that shows you what \"Context PS\" compiles to in JS.\n",
            "\n",
            "You must output the JavaScript that corresponds to this purescript.\n",
            "### Input:\n",
            "module Snapshot.EffectBindAgain where\n",
            "\n",
            "import Prelude\n",
            "import Effect (Effect)\n",
            "import Effect.Class.Console as Console\n",
            "\n",
            "test45 :: Effect Unit\n",
            "test45 = do\n",
            "  value1 <- Console.log \"foo\"\n",
            "  let a = pure value1\n",
            "  a\n",
            "\n",
            "### Context PS:\n",
            "module Snapshot.EffectBind01 where\n",
            "\n",
            "import Prelude\n",
            "import Effect (Effect)\n",
            "import Effect.Class.Console as Console\n",
            "\n",
            "test1 :: Effect Unit\n",
            "test1 = do\n",
            "  Console.log \"1\"\n",
            "  value <- Console.log \"2\"\n",
            "  Console.log \"3\"\n",
            "  pure value\n",
            "\n",
            "### Context JS:\n",
            "\n",
            "import * as Effect$dConsole from \"../Effect.Console/index.js\";\n",
            "const test1 = /* #__PURE__ */ (() => {\n",
            "  const $0 = Effect$dConsole.log(\"1\");\n",
            "  return () => {\n",
            "    $0();\n",
            "    const value = Effect$dConsole.log(\"2\")();\n",
            "    Effect$dConsole.log(\"3\")();\n",
            "    return value;\n",
            "  };\n",
            "})();\n",
            "export {test1};\n",
            "\n",
            "### Response:\n",
            "\n",
            "import * as Effect$dConsole from \"../Effect.Console/index.js\";\n",
            "const test45 = /* #__PURE__ */ (() => {\n",
            "  const $0 = Effect$dConsole.log(\"foo\");\n",
            "  return () => {\n",
            "    $0();\n",
            "    const a = Effect$dConsole.log(\"foo\")();\n",
            "    return a;\n",
            "  };\n",
            "})();\n",
            "export {test45};\n",
            "\n",
            "### Ex\n"
          ]
        }
      ],
      "source": [
        "eval_prompt = \"\"\"You are a powerful purescript-to-javascript compiler. Your job is to convert purescript code into javascript. You are given \"Input\" in purescript, \"Context PS\" showing a similar chunk of PureScript and \"Context JS\" that shows you what \"Context PS\" compiles to in JS.\n",
        "\n",
        "You must output the JavaScript that corresponds to this purescript.\n",
        "### Input:\n",
        "module Snapshot.EffectBindAgain where\n",
        "\n",
        "import Prelude\n",
        "import Effect (Effect)\n",
        "import Effect.Class.Console as Console\n",
        "\n",
        "test45 :: Effect Unit\n",
        "test45 = do\n",
        "  value1 <- Console.log \"foo\"\n",
        "  let a = pure value1\n",
        "  a\n",
        "\n",
        "### Context PS:\n",
        "module Snapshot.EffectBind01 where\n",
        "\n",
        "import Prelude\n",
        "import Effect (Effect)\n",
        "import Effect.Class.Console as Console\n",
        "\n",
        "test1 :: Effect Unit\n",
        "test1 = do\n",
        "  Console.log \"1\"\n",
        "  value <- Console.log \"2\"\n",
        "  Console.log \"3\"\n",
        "  pure value\n",
        "\n",
        "### Context JS:\n",
        "\n",
        "import * as Effect$dConsole from \"../Effect.Console/index.js\";\n",
        "const test1 = /* #__PURE__ */ (() => {\n",
        "  const $0 = Effect$dConsole.log(\"1\");\n",
        "  return () => {\n",
        "    $0();\n",
        "    const value = Effect$dConsole.log(\"2\")();\n",
        "    Effect$dConsole.log(\"3\")();\n",
        "    return value;\n",
        "  };\n",
        "})();\n",
        "export {test1};\n",
        "\n",
        "### Response:\n",
        "\"\"\"\n",
        "# {'question': 'Name the comptroller for office of prohibition', 'context': 'CREATE TABLE table_22607062_1 (comptroller VARCHAR, ticket___office VARCHAR)', 'answer': 'SELECT comptroller FROM table_22607062_1 WHERE ticket___office = \"Prohibition\"'}\n",
        "model_input = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    print(tokenizer.decode(model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "544rmsdU1CSJ"
      },
      "source": [
        "I get the output:\n",
        "```\n",
        "SELECT * FROM table_name_12 WHERE class > 91.5 AND city_of_license = 'hyannis, nebraska'\n",
        "```\n",
        "which is clearly wrong if the input is asking for just class!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "puuOXL2R1CSJ"
      },
      "source": [
        "### 4. Tokenization\n",
        "Setup some tokenization settings like left padding because it makes [training use less memory](https://ai.stackexchange.com/questions/41485/while-fine-tuning-a-decoder-only-llm-like-llama-on-chat-dataset-what-kind-of-pa):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c1P0mphA1CSJ"
      },
      "outputs": [],
      "source": [
        "tokenizer.add_eos_token = True\n",
        "tokenizer.pad_token_id = 0\n",
        "tokenizer.padding_side = \"left\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kpo66hMo1CSJ"
      },
      "source": [
        "Setup the tokenize function to make labels and input_ids the same. This is basically what [self-supervised fine-tuning](https://neptune.ai/blog/self-supervised-learning) is:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gjSerml71CSJ"
      },
      "outputs": [],
      "source": [
        "def tokenize(prompt):\n",
        "    result = tokenizer(\n",
        "        prompt,\n",
        "        truncation=True,\n",
        "        max_length=512,\n",
        "        padding=False,\n",
        "        return_tensors=None,\n",
        "    )\n",
        "\n",
        "    # \"self-supervised learning\" means the labels are also the inputs:\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNk_3THv1CSK"
      },
      "source": [
        "And run convert each data_point into a prompt that I found online that works quite well:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MvO7A-ZF1CSK"
      },
      "outputs": [],
      "source": [
        "def generate_and_tokenize_prompt(data_point):\n",
        "    full_prompt =f\"\"\"You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.\n",
        "\n",
        "You must output the SQL query that answers the question.\n",
        "\n",
        "### Input:\n",
        "{data_point[\"question\"]}\n",
        "\n",
        "### Context:\n",
        "{data_point[\"context\"]}\n",
        "\n",
        "### Response:\n",
        "{data_point[\"answer\"]}\n",
        "\"\"\"\n",
        "    return tokenize(full_prompt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLvwo-ax1CSK"
      },
      "source": [
        "Reformat to prompt and tokenize each sample:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SA2BqSzW1CSK"
      },
      "outputs": [],
      "source": [
        "tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt)\n",
        "tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5ByhitV1CSK"
      },
      "source": [
        "### 5. Setup Lora"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9q_26pz71CSK"
      },
      "outputs": [],
      "source": [
        "model.train() # put model back into training mode\n",
        "model = prepare_model_for_int8_training(model)\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=16,\n",
        "    target_modules=[\n",
        "    \"q_proj\",\n",
        "    \"k_proj\",\n",
        "    \"v_proj\",\n",
        "    \"o_proj\",\n",
        "],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "model = get_peft_model(model, config)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IvkQ3igC1CSK"
      },
      "source": [
        "To resume from a checkpoint, set resume_from_checkpoint to the path of the adapter_model.bin you want to resume from. This code'll replace the lora adapter attached to the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TMTxR2JT1CSK"
      },
      "outputs": [],
      "source": [
        "resume_from_checkpoint = \"\" # set this to the adapter_model.bin file you want to resume from\n",
        "\n",
        "if resume_from_checkpoint:\n",
        "    if os.path.exists(resume_from_checkpoint):\n",
        "        print(f\"Restarting from {resume_from_checkpoint}\")\n",
        "        adapters_weights = torch.load(resume_from_checkpoint)\n",
        "        set_peft_model_state_dict(model, adapters_weights)\n",
        "    else:\n",
        "        print(f\"Checkpoint {resume_from_checkpoint} not found\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GW1wtvbP1CSK"
      },
      "source": [
        "Optional stuff to setup Weights and Biases to view training graphs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AC1_qWbS1CSK"
      },
      "outputs": [],
      "source": [
        "wandb_project = \"sql-try2-coder\"\n",
        "if len(wandb_project) > 0:\n",
        "    os.environ[\"WANDB_PROJECT\"] = wandb_project\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5iv4txxu1CSK"
      },
      "outputs": [],
      "source": [
        "if torch.cuda.device_count() > 1:\n",
        "    # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available\n",
        "    model.is_parallelizable = True\n",
        "    model.model_parallel = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pYSsnciQ1CSK"
      },
      "source": [
        "### 6. Training arguments\n",
        "If you run out of GPU memory, change per_device_train_batch_size. The gradient_accumulation_steps variable should ensure this doesn't affect batch dynamics during the training run. All the other variables are standard stuff that I wouldn't recommend messing with:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uvEi1kP21CSK"
      },
      "outputs": [],
      "source": [
        "batch_size = 128\n",
        "per_device_train_batch_size = 32\n",
        "gradient_accumulation_steps = batch_size // per_device_train_batch_size\n",
        "output_dir = \"sql-code-llama\"\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "        per_device_train_batch_size=per_device_train_batch_size,\n",
        "        gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "        warmup_steps=100,\n",
        "        max_steps=400,\n",
        "        learning_rate=3e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=10,\n",
        "        optim=\"adamw_torch\",\n",
        "        evaluation_strategy=\"steps\", # if val_set_size > 0 else \"no\",\n",
        "        save_strategy=\"steps\",\n",
        "        eval_steps=20,\n",
        "        save_steps=20,\n",
        "        output_dir=output_dir,\n",
        "        # save_total_limit=3,\n",
        "        load_best_model_at_end=False,\n",
        "        # ddp_find_unused_parameters=False if ddp else None,\n",
        "        group_by_length=True, # group sequences of roughly the same length together to speed up training\n",
        "        report_to=\"wandb\", # if use_wandb else \"none\",\n",
        "        run_name=f\"codellama-{datetime.now().strftime('%Y-%m-%d-%H-%M')}\", # if use_wandb else None,\n",
        "    )\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_train_dataset,\n",
        "    eval_dataset=tokenized_val_dataset,\n",
        "    args=training_args,\n",
        "    data_collator=DataCollatorForSeq2Seq(\n",
        "        tokenizer, pad_to_multiple_of=8, return_tensors=\"pt\", padding=True\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1aJp6Jxl1CSK"
      },
      "source": [
        "Then we do some pytorch-related optimisation (which just make training faster but don't affect accuracy):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ycCHZZl1CSK"
      },
      "outputs": [],
      "source": [
        "model.config.use_cache = False\n",
        "\n",
        "old_state_dict = model.state_dict\n",
        "model.state_dict = (lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())).__get__(\n",
        "    model, type(model)\n",
        ")\n",
        "if torch.__version__ >= \"2\" and sys.platform != \"win32\":\n",
        "    print(\"compiling the model\")\n",
        "    model = torch.compile(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bF5oWKxK1CSL"
      },
      "outputs": [],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1dRQLMT1CSU"
      },
      "source": [
        "### Load the final checkpoint\n",
        "Now for the moment of truth! Has our work paid off...?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sRdVgDTg1CSU"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "base_model = \"codellama/CodeLlama-7b-hf\"\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model,\n",
        "    load_in_8bit=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"codellama/CodeLlama-7b-hf\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76UlHzhy1CSU"
      },
      "source": [
        "To load a fine-tuned Lora/Qlora adapter use PeftModel.from_pretrained. ```output_dir``` should be something containing an adapter_config.json and adapter_model.bin:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rQrCR0os1CSU"
      },
      "outputs": [],
      "source": [
        "from peft import PeftModel\n",
        "model = PeftModel.from_pretrained(model, output_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roqy_WRi1CSU"
      },
      "source": [
        "Try the same prompt as before:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GrCxouNp1CSU"
      },
      "outputs": [],
      "source": [
        "eval_prompt = \"\"\"You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.\n",
        "\n",
        "You must output the SQL query that answers the question.\n",
        "### Input:\n",
        "Which Class has a Frequency MHz larger than 91.5, and a City of license of hyannis, nebraska?\n",
        "\n",
        "### Context:\n",
        "CREATE TABLE table_name_12 (class VARCHAR, frequency_mhz VARCHAR, city_of_license VARCHAR)\n",
        "\n",
        "### Response:\n",
        "\"\"\"\n",
        "\n",
        "model_input = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    print(tokenizer.decode(model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I6IcTOCq1CSU"
      },
      "source": [
        "And the model outputs:\n",
        "```\n",
        "SELECT class FROM table_name_12 WHERE frequency_mhz > 91.5 AND city_of_license = \"hyannis, nebraska\"\n",
        "```\n",
        "So it works! If you want to convert your this adapter to a Llama.cpp model to run locally follow my other [guide](https://ragntune.com/blog/A-guide-to-running-Llama-2-qlora-loras-on-Llama.cpp). If you have any questions, shoot me a message on [Elon Musk's website](https://twitter.com/samlhuillier_).\n"
      ]
    }
  ],
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