sql_generation_finetuning.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "6jA-eU6gNgFm"
   },
   "source": [
    "# **Installing all of the packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1o2x-tMzNHzf",
    "outputId": "4f518490-5e2b-4361-9f47-7ece28057fee"
   },
   "outputs": [],
   "source": [
    "!pip install -U accelerate bitsandbytes peft transformers datasets trl git-lfs wandb flash-attn sql-metadata scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nPUstk6XO4Cj",
    "outputId": "29264a7b-9b96-4a35-8c29-fa6e80a213d7"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import pandas as pd\n",
    "from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer, TrainingArguments\n",
    "from peft import LoraConfig, TaskType\n",
    "from datasets import load_dataset\n",
    "from sql_metadata import Parser\n",
    "from trl import SFTTrainer, DataCollatorForCompletionOnlyLM\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "0HTMC3T8OzR6"
   },
   "source": [
    "# **Connect to wandb**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "zOzNA3_VO9jI"
   },
   "outputs": [],
   "source": [
    "os.environ[\"WANDB_PROJECT\"]=\"deepseek_full_finetuning\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "K0tM3XIxPJI5"
   },
   "source": [
    "# **Loading the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "JhnQBSBHQ7G-"
   },
   "outputs": [],
   "source": [
    "model_name = \"deepseek-ai/deepseek-coder-6.7b-instruct\"\n",
    "# model_name = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
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    "outputId": "9080f48c-7619-40e8-95df-e63005c6ea76"
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   "outputs": [],
   "source": [
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_compute_dtype = torch.float16,\n",
    "    bnb_4bit_quant_type='nf4',\n",
    "    bnb_4bit_use_double_quant = True\n",
    ")\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    #quantization_config=bnb_config,\n",
    "    attn_implementation=\"flash_attention_2\",\n",
    "    torch_dtype = torch.bfloat16,\n",
    "    device_map='auto',\n",
    ")\n",
    "model.config.use_cache = False\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
    "tokenizer.padding_side = \"right\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C4hc1owIZ4iQ",
    "outputId": "df121a2d-d9de-4604-91e2-9a14dcedcc82"
   },
   "outputs": [],
   "source": [
    "print(model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "3zPYXaFKSJpr"
   },
   "source": [
    "# **Loading the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
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    },
    "id": "_JGZOxzjlNNJ",
    "outputId": "0dba676b-e06c-4bc6-cd6c-d3288475b6a1"
   },
   "outputs": [],
   "source": [
    "data_files = {\"train\": \"full_finetuning_dataset.csv\", \"validation\": \"validation_dataset_formatted.csv\"}\n",
    "dataset = load_dataset('csv', data_files=data_files)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "gXMxNog0ldg8"
   },
   "source": [
    "**Filtering rows with max tokens**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "QBKWqHPGUMYh"
   },
   "outputs": [],
   "source": [
    "def formatting_prompts_func(training_dataset):\n",
    "  output_texts = []\n",
    "  for i in range(len(training_dataset['question'])):\n",
    "    question = training_dataset['question'][i]\n",
    "    query = training_dataset['query'][i]\n",
    "    database_schema = training_dataset['database_schema'][i]\n",
    "    user_message = f\"\"\"Given the following SQL tables, your job is to generate the Sqlite SQL query given the user's question.\n",
    "Put your answer inside the ```sql and ``` tags.\n",
    "{database_schema}\n",
    "###\n",
    "Question: {question}\n",
    "\"\"\"\n",
    "    assitant_message = f\"\"\"\n",
    "```sql\n",
    "{query} ;\n",
    "```\n",
    "<|EOT|>\n",
    "\"\"\"\n",
    "    messages = [\n",
    "      {\"role\": \"user\", \"content\": user_message},\n",
    "      {\"role\": \"assistant\", \"content\": assitant_message},\n",
    "      ]\n",
    "    text = tokenizer.apply_chat_template(messages, tokenize=False)\n",
    "    output_texts.append(text)\n",
    "  return output_texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "R-MltV_bXd-f"
   },
   "outputs": [],
   "source": [
    "response_template = \"### Response:\" #deepseek\n",
    "# response_template = \"[/INST]\" #mistral\"\n",
    "collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "WZsE5cbRXsZg"
   },
   "source": [
    "# **Training Config**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "J03FAyLTXrro"
   },
   "outputs": [],
   "source": [
    "lora_r = 64\n",
    "lora_alpha = 32\n",
    "lora_dropout = 0.1\n",
    "output_dir = \"./SFT\"\n",
    "num_train_epochs = 3\n",
    "bf16 = True\n",
    "overwrite_output_dir = True\n",
    "per_device_train_batch_size = 2\n",
    "per_device_eval_batch_size = 2\n",
    "gradient_accumulation_steps = 16\n",
    "gradient_checkpointing = True\n",
    "evaluation_strategy = \"steps\"\n",
    "learning_rate = 5e-5\n",
    "weight_decay = 0.01\n",
    "lr_scheduler_type = \"cosine\"\n",
    "warmup_ratio = 0.01\n",
    "max_grad_norm = 0.3\n",
    "group_by_length = True\n",
    "auto_find_batch_size = False\n",
    "save_steps = 50\n",
    "logging_steps = 50\n",
    "load_best_model_at_end= False\n",
    "packing = False\n",
    "save_total_limit=3\n",
    "neftune_noise_alpha=5\n",
    "report_to=\"wandb\"\n",
    "max_seq_length = 2100 #set based on the maximum number of queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "f5GAxJ2NZ3V0"
   },
   "outputs": [],
   "source": [
    "peft_config = LoraConfig(\n",
    "    lora_alpha=lora_alpha,\n",
    "    lora_dropout=lora_dropout,\n",
    "    r=lora_r,\n",
    "    target_modules=[\n",
    "        \"q_proj\",\n",
    "        \"v_proj\",\n",
    "        \"k_proj\",\n",
    "        \"o_proj\",\n",
    "        \"gate_proj\",\n",
    "        \"up_proj\",\n",
    "        \"down_proj\",\n",
    "        \"lm_head\"\n",
    "    ],\n",
    "    task_type=TaskType.CAUSAL_LM,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "w5J96g5qZ-qO"
   },
   "outputs": [],
   "source": [
    "training_arguments = TrainingArguments(\n",
    "    output_dir=output_dir,\n",
    "    overwrite_output_dir=overwrite_output_dir,\n",
    "    num_train_epochs=num_train_epochs,\n",
    "    load_best_model_at_end=load_best_model_at_end,\n",
    "    per_device_train_batch_size=per_device_train_batch_size,\n",
    "    evaluation_strategy=evaluation_strategy,\n",
    "    max_grad_norm = max_grad_norm,\n",
    "    auto_find_batch_size = auto_find_batch_size,\n",
    "    save_total_limit = save_total_limit,\n",
    "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
    "    save_steps=save_steps,\n",
    "    logging_steps=logging_steps,\n",
    "    learning_rate=learning_rate,\n",
    "    weight_decay=weight_decay,\n",
    "    bf16=bf16,\n",
    "    warmup_ratio=warmup_ratio,\n",
    "    group_by_length=group_by_length,\n",
    "    lr_scheduler_type=lr_scheduler_type,\n",
    "    report_to=report_to,\n",
    "    neftune_noise_alpha= neftune_noise_alpha\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
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   "outputs": [],
   "source": [
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset['train'],\n",
    "    eval_dataset=dataset['validation'],\n",
    "    peft_config=peft_config,\n",
    "    formatting_func=formatting_prompts_func,\n",
    "    data_collator=collator,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
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