fine-tune-code-llama.ipynb

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      "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": "0cd5de72-1e8e-4e78-a99d-91bc439b6a25"
      },
      "outputs": [],
      "source": [
        "!pip install git+https://github.com/huggingface/transformers.git@main bitsandbytes  # we need latest transformers for this\n",
        "!pip install git+https://github.com/huggingface/peft.git@4c611f4\n",
        "!pip install datasets==2.10.1\n",
        "import locale # colab workaround\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\" # colab workaround\n",
        "!pip install wandb\n",
        "!pip install scipy"
      ]
    },
    {
      "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",
      "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"
      ]
    },
    {
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        "outputId": "01a36c98-1e43-44f6-d5c6-1a9bf908c933"
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      "outputs": [],
      "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": "90758279-19ee-48cc-8d52-8c96902e08c2"
      },
      "outputs": [],
      "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:"
      ]
    },
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        "id": "rMnU93bY1CSJ",
        "outputId": "2081b3ab-4073-4687-d888-e1b7ec0e7548"
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      "outputs": [],
      "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",
        "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": "c26db817-df1a-4776-fc6e-7e3a78ba1238"
      },
      "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",
        "# {'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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "id": "SA2BqSzW1CSK",
        "outputId": "7d1c857f-f063-466b-e37d-f01d89c924b2"
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      "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": "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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