sql_generation_finetuning.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "6jA-eU6gNgFm"
   },
   "source": [
    "# **Installing all of the packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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==4.39 datasets trl git-lfs wandb flash-attn sql-metadata scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nPUstk6XO4Cj",
    "outputId": "29264a7b-9b96-4a35-8c29-fa6e80a213d7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-23 10:23:37,647 - modelscope - INFO - PyTorch version 2.0.1 Found.\n",
      "2024-04-23 10:23:37,650 - modelscope - INFO - Loading ast index from /hpc2hdd/home/jzhao815/.cache/modelscope/ast_indexer\n",
      "2024-04-23 10:23:37,698 - modelscope - INFO - Loading done! Current index file version is 1.13.0, with md5 8962eeb014e66d8db494c341ba0f48c1 and a total number of 972 components indexed\n"
     ]
    }
   ],
   "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\n",
    "from modelscope import AutoModelForCausalLM, AutoTokenizer"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "0HTMC3T8OzR6"
   },
   "source": [
    "# **Connect to wandb**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "zOzNA3_VO9jI"
   },
   "outputs": [],
   "source": [
    "os.environ[\"WANDB_PROJECT\"]=\"qwen_spider_finetuning\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "K0tM3XIxPJI5"
   },
   "source": [
    "# **Loading the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "JhnQBSBHQ7G-"
   },
   "outputs": [],
   "source": [
    "model_name = \"qwen/CodeQwen1.5-7B-Chat\"\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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    },
    "id": "3AOh9RibPLXF",
    "outputId": "9080f48c-7619-40e8-95df-e63005c6ea76"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aad61287751042239e67c172e1176e95",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C4hc1owIZ4iQ",
    "outputId": "df121a2d-d9de-4604-91e2-9a14dcedcc82"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qwen2ForCausalLM(\n",
      "  (model): Qwen2Model(\n",
      "    (embed_tokens): Embedding(92416, 4096)\n",
      "    (layers): ModuleList(\n",
      "      (0-31): 32 x Qwen2DecoderLayer(\n",
      "        (self_attn): Qwen2FlashAttention2(\n",
      "          (q_proj): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "          (k_proj): Linear(in_features=4096, out_features=512, bias=True)\n",
      "          (v_proj): Linear(in_features=4096, out_features=512, bias=True)\n",
      "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
      "          (rotary_emb): Qwen2RotaryEmbedding()\n",
      "        )\n",
      "        (mlp): Qwen2MLP(\n",
      "          (gate_proj): Linear(in_features=4096, out_features=13440, bias=False)\n",
      "          (up_proj): Linear(in_features=4096, out_features=13440, bias=False)\n",
      "          (down_proj): Linear(in_features=13440, out_features=4096, bias=False)\n",
      "          (act_fn): SiLU()\n",
      "        )\n",
      "        (input_layernorm): Qwen2RMSNorm()\n",
      "        (post_attention_layernorm): Qwen2RMSNorm()\n",
      "      )\n",
      "    )\n",
      "    (norm): Qwen2RMSNorm()\n",
      "  )\n",
      "  (lm_head): Linear(in_features=4096, out_features=92416, bias=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "3zPYXaFKSJpr"
   },
   "source": [
    "# **Loading the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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\": \"./training/spider_filtered_finetuning_dataset.csv\", \"validation\": \"./validation/filtered_spider_syn_dataset.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": 8,
   "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['filtered_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": 9,
   "metadata": {
    "id": "R-MltV_bXd-f"
   },
   "outputs": [],
   "source": [
    "# response_template = \"### Response:\" #deepseek\n",
    "# response_template = \"[/INST]\" #mistral\"\n",
    "response_template = \"<|im_start|>\" #Qwencode\n",
    "collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "WZsE5cbRXsZg"
   },
   "source": [
    "# **Training Config**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "J03FAyLTXrro"
   },
   "outputs": [],
   "source": [
    "lora_r = 64\n",
    "lora_alpha = 32\n",
    "lora_dropout = 0.1\n",
    "output_dir = \"./SFT_stage2\"\n",
    "num_train_epochs = 3\n",
    "bf16 = True\n",
    "overwrite_output_dir = True\n",
    "per_device_train_batch_size = 16\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 = True\n",
    "save_steps = 100\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": 12,
   "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": 13,
   "metadata": {
    "id": "w5J96g5qZ-qO"
   },
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   "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": 14,
   "metadata": {
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       "Map:   0%|          | 0/8412 [00:00<?, ? examples/s]"
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       "Map:   0%|          | 0/1034 [00:00<?, ? examples/s]"
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     "output_type": "stream",
     "text": [
      "/hpc2hdd/home/jzhao815/.conda/envs/dbgpt_hub/lib/python3.10/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
      "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
      "  warnings.warn(\n",
      "Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    }
   ],
   "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",
    "    max_seq_length=max_seq_length,\n",
    "    packing=packing\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001B[34m\u001B[1mwandb\u001B[0m: Currently logged in as: \u001B[33mjzhao815\u001B[0m (\u001B[33mzjj2323\u001B[0m). Use \u001B[1m`wandb login --relogin`\u001B[0m to force relogin\n"
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     "data": {
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       "Tracking run with wandb version 0.16.6"
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      "text/plain": [
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       "Run data is saved locally in <code>/hpc2hdd/home/jzhao815/6000E_code/wandb/run-20240423_102658-wibx1qg5</code>"
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       "Syncing run <strong><a href='https://wandb.ai/zjj2323/qwen_spider_finetuning/runs/wibx1qg5' target=\"_blank\">genial-snow-1</a></strong> to <a href='https://wandb.ai/zjj2323/qwen_spider_finetuning' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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       " View project at <a href='https://wandb.ai/zjj2323/qwen_spider_finetuning' target=\"_blank\">https://wandb.ai/zjj2323/qwen_spider_finetuning</a>"
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='524' max='524' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [524/524 1:02:06, Epoch 3/4]\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",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.454700</td>\n",
       "      <td>0.089952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.082700</td>\n",
       "      <td>0.084899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>0.065600</td>\n",
       "      <td>0.073222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>0.049800</td>\n",
       "      <td>0.073616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250</td>\n",
       "      <td>0.046400</td>\n",
       "      <td>0.070646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0.039300</td>\n",
       "      <td>0.070832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
       "      <td>0.037000</td>\n",
       "      <td>0.069895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.033200</td>\n",
       "      <td>0.070640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>0.032500</td>\n",
       "      <td>0.070813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.031800</td>\n",
       "      <td>0.070866</td>\n",
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     "text": [
      "/hpc2hdd/home/jzhao815/.conda/envs/dbgpt_hub/lib/python3.10/site-packages/peft/utils/save_and_load.py:139: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n",
      "  warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n",
      "/hpc2hdd/home/jzhao815/.conda/envs/dbgpt_hub/lib/python3.10/site-packages/peft/utils/save_and_load.py:139: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n",
      "  warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n",
      "/hpc2hdd/home/jzhao815/.conda/envs/dbgpt_hub/lib/python3.10/site-packages/peft/utils/save_and_load.py:139: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n",
      "  warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n",
      "/hpc2hdd/home/jzhao815/.conda/envs/dbgpt_hub/lib/python3.10/site-packages/peft/utils/save_and_load.py:139: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n",
      "  warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n",
      "/hpc2hdd/home/jzhao815/.conda/envs/dbgpt_hub/lib/python3.10/site-packages/peft/utils/save_and_load.py:139: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n",
      "  warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=524, training_loss=0.08456814539341526, metrics={'train_runtime': 3738.2041, 'train_samples_per_second': 9.001, 'train_steps_per_second': 0.14, 'total_flos': 4.9921790327537664e+17, 'train_loss': 0.08456814539341526, 'epoch': 3.99})"
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 16,
   "metadata": {
    "id": "Wldw2uGY_8M9"
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     "output_type": "stream",
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      "/hpc2hdd/home/jzhao815/.conda/envs/dbgpt_hub/lib/python3.10/site-packages/peft/utils/save_and_load.py:139: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n",
      "  warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n"
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   "source": [
    "output_dir = os.path.join(\"./\", \"final_checkpoint_codeQwen2\")\n",
    "trainer.model.save_pretrained(output_dir)"
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