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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/defog-ai/sqlcoder/blob/main/defog-sqlcoder-colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "dba7aed5-db78-4a4a-ee77-222a9129844e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
            "Collecting transformers\n",
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            "\u001b[?25hCollecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.41.1-py3-none-any.whl (92.6 MB)\n",
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            "\u001b[?25hCollecting accelerate\n",
            "  Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.2)\n",
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            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.27.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.6)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m83.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Installing collected packages: tokenizers, safetensors, bitsandbytes, huggingface-hub, transformers, accelerate\n",
            "Successfully installed accelerate-0.21.0 bitsandbytes-0.41.1 huggingface-hub-0.16.4 safetensors-0.3.2 tokenizers-0.13.3 transformers-4.31.0\n"
          ]
        }
      ],
      "source": [
        "!pip install torch transformers bitsandbytes accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline"
      ],
      "metadata": {
        "id": "BzllQomZfQnj"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.is_available()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hztT0MXkfRs1",
        "outputId": "93300c3e-4119-4cab-ab97-7b1f1c86ffef"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# use an A100 on Colab Pro (or any system with >30GB VRAM on your own machine) to load this in bf16\n",
        "# if not available, use a GPU with minimum 20GB VRAM to load this in 8bit, or with minimum 12GB of VRAM to load in 4bit\n",
        "# on Colab, works with a V100 but crashes on a T4\n",
        "\n",
        "# downloading the model and then loading it to memory step takes around 10 minutes the first time. Be patient :)"
      ],
      "metadata": {
        "id": "Acq5rPZjfWAi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"defog/sqlcoder\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    trust_remote_code=True,\n",
        "    # torch_dtype=torch.bfloat16,\n",
        "    # load_in_8bit=True,\n",
        "    load_in_4bit=True,\n",
        "    device_map=\"auto\",\n",
        "    use_cache=True,\n",
        ")"
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      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"What products has the biggest fall in sales in 2022 compared to 2021? Give me the product name, the sales amount in both years, and the difference.\""
      ],
      "metadata": {
        "id": "0lnutc3DfSep"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"### Instructions:\n",
        "Your task is convert a question into a SQL query, given a Postgres database schema.\n",
        "Adhere to these rules:\n",
        "- **Deliberately go through the question and database schema word by word** to appropriately answer the question\n",
        "- **Use Table Aliases** to prevent ambiguity. For example, `SELECT table1.col1, table2.col1 FROM table1 JOIN table2 ON table1.id = table2.id`.\n",
        "- When creating a ratio, always cast the numerator as float\n",
        "\n",
        "### Input:\n",
        "Generate a SQL query that answers the questioq `{question}`.\n",
        "This query will run on a database whose schema is represented in this string:\n",
        "CREATE TABLE products (\n",
        "  product_id INTEGER PRIMARY KEY, -- Unique ID for each product\n",
        "  name VARCHAR(50), -- Name of the product\n",
        "  price DECIMAL(10,2), -- Price of each unit of the product\n",
        "  quantity INTEGER  -- Current quantity in stock\n",
        ");\n",
        "\n",
        "CREATE TABLE customers (\n",
        "   customer_id INTEGER PRIMARY KEY, -- Unique ID for each customer\n",
        "   name VARCHAR(50), -- Name of the customer\n",
        "   address VARCHAR(100) -- Mailing address of the customer\n",
        ");\n",
        "\n",
        "CREATE TABLE salespeople (\n",
        "  salesperson_id INTEGER PRIMARY KEY, -- Unique ID for each salesperson\n",
        "  name VARCHAR(50), -- Name of the salesperson\n",
        "  region VARCHAR(50) -- Geographic sales region\n",
        ");\n",
        "\n",
        "CREATE TABLE sales (\n",
        "  sale_id INTEGER PRIMARY KEY, -- Unique ID for each sale\n",
        "  product_id INTEGER, -- ID of product sold\n",
        "  customer_id INTEGER,  -- ID of customer who made purchase\n",
        "  salesperson_id INTEGER, -- ID of salesperson who made the sale\n",
        "  sale_date DATE, -- Date the sale occurred\n",
        "  quantity INTEGER -- Quantity of product sold\n",
        ");\n",
        "\n",
        "CREATE TABLE product_suppliers (\n",
        "  supplier_id INTEGER PRIMARY KEY, -- Unique ID for each supplier\n",
        "  product_id INTEGER, -- Product ID supplied\n",
        "  supply_price DECIMAL(10,2) -- Unit price charged by supplier\n",
        ");\n",
        "\n",
        "-- sales.product_id can be joined with products.product_id\n",
        "-- sales.customer_id can be joined with customers.customer_id\n",
        "-- sales.salesperson_id can be joined with salespeople.salesperson_id\n",
        "-- product_suppliers.product_id can be joined with products.product_id\n",
        "\n",
        "### Response:\n",
        "Based on your instructions, here is the SQL query I have generated to answer the question `{question}`:\n",
        "```sql\n",
        "\"\"\".format(question=question)"
      ],
      "metadata": {
        "id": "0eI-VpCkf-fN"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eos_token_id = tokenizer.convert_tokens_to_ids([\"```\"])[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2HObbLBgF6M",
        "outputId": "4e306439-5b63-4c78-9374-0b91dfb753da"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# excruciatingly slow on an V100 with 4bit quantization\n",
        "# takes around 1-2 minutes per query\n",
        "# on a single A100 40GB, takes ~10-20 seconds"
      ],
      "metadata": {
        "id": "MmRXVrXgmBgg"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "generated_ids = model.generate(\n",
        "    **inputs,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=eos_token_id,\n",
        "    pad_token_id=eos_token_id,\n",
        "    max_new_tokens=400,\n",
        "    do_sample=False,\n",
        "    num_beams=5\n",
        ")"
      ],
      "metadata": {
        "id": "ZpbVgVHMkJvC"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)"
      ],
      "metadata": {
        "id": "f_tdyghyk6jB"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()\n",
        "# empty cache so that you do generate more results w/o memory crashing\n",
        "# particularly important on Colab – memory management is much more straightforward\n",
        "# when running on an inference service"
      ],
      "metadata": {
        "id": "VjiJDf0dl0XD"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(outputs[0].split(\"```sql\")[-1].split(\"```\")[0].split(\";\")[0].strip() + \";\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCJvcurqlP2_",
        "outputId": "fe721931-cd60-47dd-ec6c-4f9d99f5b7fa"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WITH sales_2021 AS (\n",
            "  SELECT sales.product_id,\n",
            "         sum(sales.quantity) AS sales_2021\n",
            "  FROM   sales\n",
            "  WHERE  sales.sale_date >= '2021-01-01'\n",
            "     AND sales.sale_date <= '2021-12-31'\n",
            "  GROUP BY sales.product_id\n",
            "), sales_2022 AS (\n",
            "  SELECT sales.product_id,\n",
            "         sum(sales.quantity) AS sales_2022\n",
            "  FROM   sales\n",
            "  WHERE  sales.sale_date >= '2022-01-01'\n",
            "     AND sales.sale_date <= '2022-12-31'\n",
            "  GROUP BY sales.product_id\n",
            ")\n",
            "SELECT products.name,\n",
            "       sales_2021.sales_2021,\n",
            "       sales_2022.sales_2022,\n",
            "       sales_2022.sales_2022 - sales_2021.sales_2021 AS difference\n",
            "FROM   products\n",
            "    LEFT JOIN sales_2021 ON products.product_id = sales_2021.product_id\n",
            "    LEFT JOIN sales_2022 ON products.product_id = sales_2022.product_id\n",
            "ORDER BY difference DESC NULLS LAST;\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wDnRg0zRlQs2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}