{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "kH18jD5cR_Ks",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "bab0bba5-68fd-47d0-ea8f-d256c3d1fa15"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.1/44.1 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.7/320.7 kB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m310.9/310.9 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m53.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.0/40.0 MB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m43.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 18.0.0 which is incompatible.\n",
"ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 18.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m"
]
}
],
"source": [
"# !pip install -q accelerate peft bitsandbytes transformers trl faiss-gpu langchain_community wandb flash-attn\n",
"!pip install -q accelerate peft bitsandbytes transformers trl datasets\n",
"\n",
"# flash-attn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "cgVNTbBa-D3j"
},
"outputs": [],
"source": [
"# load the required packages.\n",
"import torch\n",
"from datasets import load_dataset, Dataset\n",
"from peft import LoraConfig, AutoPeftModelForCausalLM, PeftModel, get_peft_model\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, AutoConfig, set_seed\n",
"from trl import SFTTrainer\n",
"import bitsandbytes as bnb\n",
"import transformers\n",
"\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import sqlparse\n",
"import re\n",
"import json\n",
"\n",
"from huggingface_hub import hf_hub_download\n",
"from huggingface_hub import HfFileSystem"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s7RtbLxPwTx2",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "43d5ff02-3e33-439c-e3a9-c18ce99d237d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"# from google.colab import drive\n",
"# from google.colab import userdata\n",
"# drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "basaX_55Yf_D"
},
"outputs": [],
"source": [
"#transformers.logging.set_verbosity_info()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "bkkjgGdlrNcq"
},
"outputs": [],
"source": [
"WRITE_TOKEN = userdata.get('hf_write')\n",
"READ_TOKEN = userdata.get('hf_read')\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "7CKnwlRfZj4V"
},
"outputs": [],
"source": [
"model_name = \"stabilityai/stable-code-instruct-3b\"\n",
"out_name = \"lleticiasilvaa/StableCode-text2SQL-schemaReduzidoTabelas\"\n",
"prev_checkpoint = None # \"checkpoint-3000\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9zs7nCmt-pMC"
},
"outputs": [],
"source": [
"#!huggingface-cli login"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "PZdnxs8k-Cgl"
},
"outputs": [],
"source": [
"bird_id=\"NESPED-GEN/bird\"\n",
"spider_id=\"NESPED-GEN/spider_selector_schemaReduzido\"\n",
"\n",
"selector_id = \"NESPED-GEN/selector_testes\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xT2iRdCN_MFH"
},
"source": [
"### Load Data\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "lLKgY40efdJo",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 281,
"referenced_widgets": [
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"bcc0fee091bb4a308e9c39e2ae091acd",
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"29717b10d1bd45cba1a785123bd2a0b4",
"319f0c7c10c546b3a1be237d93ebf5ae",
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]
},
"outputId": "57b279a5-52e6-4a12-d22a-7fe5769c1e28"
},
"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": [
"README.md: 0%| | 0.00/885 [00:00/n/' + system_message + '/n//n/' }}\n",
"# {% if messages|selectattr(\"role\", \"equalto\", \"example\")|list %}\n",
"# Below are some examples of question and their corresponding SQL queries:/n//n/\n",
"# {% else %}\n",
"# /n/\n",
"# {% endif %}\n",
"# {% for message in loop_messages %}\n",
"# {% if message['role'] == 'example' %}\n",
"# {{ message['content'] }}/n//n/\n",
"# {% elif message['role'] == 'schema' %}\n",
"# # <|schema|>/n/The query will run on a database with the following schema:/n/{{ message['content'] }}/n//n/\n",
"# {% elif message['role'] == 'user' %}\n",
"# # <|user|>/n/[QUESTION]{{ message['content'] }}[/QUESTION]/n//n/\n",
"# {% elif message['role'] == 'assistant' %}\n",
"# # <|assistant|>/n/[SQL]{{ message['content'] }}[/SQL]\n",
"# {% endif %}\n",
"# {% endfor %}\n",
"# {% if add_generation_prompt %}\n",
"# # <|assistant|>/n/[SQL]\n",
"# {% endif %}\n",
"# \"\"\".replace(\"\\n\",\"\").replace(\" \", \"\").replace(\"/n/\", \"\\n\")\n"
],
"metadata": {
"id": "Jiq-S5I1KOBr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aHGTJNq2AmUs"
},
"outputs": [],
"source": [
"def to_sql(query):\n",
" return sqlparse.format(query, reindent=True, keyword_case='upper')\n",
"\n",
"def replace_last_sql_content(text, new_content):\n",
" matches = list(re.finditer(r'```sql (.*?)```', text, re.DOTALL))\n",
" if not matches:\n",
" return text\n",
"\n",
" last_match = matches[-1]\n",
"\n",
" start, end = last_match.span(1)\n",
" return text[:start] + new_content + text[end:]\n",
"\n",
"# def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n",
"# question = row['question_en']\n",
"# schema = row['schema_llm']\n",
"# sql = to_sql(row['query_llm'])\n",
"\n",
"# system = \"Given a user question and the schema of a database, your task is to generate an SQL query that accurately answers the question based on the provided schema.\"\n",
"\n",
"\n",
"# chat = [{'role': 'user', 'content': f\"# System:\\n{system}\\n\\n# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n",
"# {'role': 'assistant', 'content': f\"```sql\\n{sql}\\n```\"}\n",
"# ]\n",
"\n",
"# row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n",
"\n",
"# return row\n",
"\n",
"def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n",
" question = row['question_en']\n",
" schema = row['schema_llm_t']\n",
" schema_linking = to_sql(row['selector_correct'])\n",
"\n",
" system = \"Given a user question and the schema of a database, your task is to generate an JSON with the the names of tables and columns of the schema that the question is referring to.\"\n",
"\n",
" chat = [{'role': 'user', 'content': f\"# System:\\n{system}\\n\\n# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n",
" {'role': 'assistant', 'content': f\"```json\\n{schema_linking}\\n```\"}\n",
" ]\n",
"\n",
" row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n",
"\n",
" return row"
]
},
{
"cell_type": "code",
"source": [
"# spider_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/spider/spider_chain.json\", \"r\"))\n",
"# bird_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/bird/bird_chain.json\", \"r\"))"
],
"metadata": {
"id": "QlA1kMj83Vql"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# df['chain'] = spider_chain + bird_chain"
],
"metadata": {
"id": "pCcD4TiC3rpb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yvcMZAL3E3TB"
},
"outputs": [],
"source": [
"df = df.apply(apply_template, axis=1)"
]
},
{
"cell_type": "code",
"source": [
"df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 539
},
"id": "rQ0KUl2w58K9",
"outputId": "664b4e86-fe9f-45ee-bd2b-ea021a31b08f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" db_id schema \\\n",
"0 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n",
"1 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n",
"2 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n",
"3 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n",
"4 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n",
"\n",
" question_en hardness \\\n",
"0 How many heads of the departments are older th... easy \n",
"1 List the name, born state and age of the heads... medium \n",
"2 List the creation year, name and budget of eac... medium \n",
"3 What are the maximum and minimum budget of the... medium \n",
"4 What is the average number of employees of the... easy \n",
"\n",
" schema_llm \\\n",
"0 Table department (\\n department.Department_... \n",
"1 Table department (\\n department.Department_... \n",
"2 Table department (\\n department.Department_... \n",
"3 Table department (\\n department.Department_... \n",
"4 Table department (\\n department.Department_... \n",
"\n",
" query_llm \\\n",
"0 SELECT count(*) FROM head WHERE age > 56 \n",
"1 SELECT name , born_state , age FROM head ORD... \n",
"2 SELECT creation , name , budget_in_billions ... \n",
"3 SELECT max(budget_in_billions) , min(budget_i... \n",
"4 SELECT avg(num_employees) FROM department WHER... \n",
"\n",
" selector \\\n",
"0 {\\n 'head': ['head_id', 'age']\\n} \n",
"1 {\\n 'head': ['head_id', 'name', 'born_state',... \n",
"2 {\\n 'department': ['department_id', 'name', '... \n",
"3 {\\n 'department': ['department_id', 'budget_i... \n",
"4 {\\n 'department': ['department_id', 'ranking'... \n",
"\n",
" schema_llm_ct \\\n",
"0 CREATE TABLE department (\\n department.Depa... \n",
"1 CREATE TABLE department (\\n department.Depa... \n",
"2 CREATE TABLE department (\\n department.Depa... \n",
"3 CREATE TABLE department (\\n department.Depa... \n",
"4 CREATE TABLE department (\\n department.Depa... \n",
"\n",
" schema_llm_columns_min \\\n",
"0 Table department (\\n department.department_... \n",
"1 Table department (\\n department.department_... \n",
"2 Table department (\\n department.department_... \n",
"3 Table department (\\n department.department_... \n",
"4 Table department (\\n department.department_... \n",
"\n",
" schema_llm_columns_min_ct \\\n",
"0 CREATE TABLE department (\\n department.depa... \n",
"1 CREATE TABLE department (\\n department.depa... \n",
"2 CREATE TABLE department (\\n department.depa... \n",
"3 CREATE TABLE department (\\n department.depa... \n",
"4 CREATE TABLE department (\\n department.depa... \n",
"\n",
" schema_llm_all_min \\\n",
"0 Table department (\\n department.department_... \n",
"1 Table department (\\n department.department_... \n",
"2 Table department (\\n department.department_... \n",
"3 Table department (\\n department.department_... \n",
"4 Table department (\\n department.department_... \n",
"\n",
" schema_llm_all_min_ct \\\n",
"0 CREATE TABLE department (\\n department.depa... \n",
"1 CREATE TABLE department (\\n department.depa... \n",
"2 CREATE TABLE department (\\n department.depa... \n",
"3 CREATE TABLE department (\\n department.depa... \n",
"4 CREATE TABLE department (\\n department.depa... \n",
"\n",
" schema_dict \\\n",
"0 {\\n 'department': ['Department_ID', 'Name', '... \n",
"1 {\\n 'department': ['Department_ID', 'Name', '... \n",
"2 {\\n 'department': ['Department_ID', 'Name', '... \n",
"3 {\\n 'department': ['Department_ID', 'Name', '... \n",
"4 {\\n 'department': ['Department_ID', 'Name', '... \n",
"\n",
" selector_correct \\\n",
"0 {\\n 'head': ['head_ID', 'age']\\n} \n",
"1 {\\n 'head': ['head_ID', 'name', 'born_state',... \n",
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"text": [
"Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
"Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
" return fn(*args, **kwargs)\n",
"/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
" with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n",
"Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
"Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
" return fn(*args, **kwargs)\n",
"/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
" with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n"
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"text": [
"Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
"Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
" return fn(*args, **kwargs)\n",
"/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
" with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n",
"Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
"Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
" return fn(*args, **kwargs)\n",
"/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
" with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n"
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"source": [
"steps=250\n",
"strategy=\"steps\"\n",
"\n",
"trainer = SFTTrainer(\n",
" model= model,\n",
" tokenizer=tokenizer,\n",
"\n",
" train_dataset=train_dataset,\n",
" eval_dataset=valid_dataset,\n",
"\n",
" peft_config=peft_config,\n",
"\n",
" dataset_text_field=\"text\",\n",
" max_seq_length=2048,\n",
" args= TrainingArguments(\n",
" output_dir=\"out\",\n",
"\n",
" save_strategy=strategy,\n",
" save_steps= steps,\n",
"\n",
" logging_strategy=strategy,\n",
" logging_steps=steps,\n",
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"\n",
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" num_train_epochs=1,\n",
" per_device_train_batch_size=1,\n",
" per_device_eval_batch_size=1,\n",
" gradient_accumulation_steps=8, #primeira versão era 1\n",
" gradient_checkpointing=True,\n",
" fp16=True,\n",
" bf16=False,\n",
"\n",
" optim=\"paged_adamw_8bit\",\n",
" learning_rate=1e-4, #primeira versão era 2e-4,\n",
" weight_decay=0.001,\n",
" max_grad_norm=0.3,\n",
" max_steps=-1,\n",
" warmup_ratio=0.03,\n",
" group_by_length=True,\n",
" lr_scheduler_type=\"cosine\", #primeira versão era linear\n",
" seed= seed,\n",
"\n",
"\n",
" report_to=[\"tensorboard\"],\n",
" push_to_hub=True,\n",
" hub_strategy=\"all_checkpoints\",\n",
" hub_model_id=out_name,\n",
"\n",
" label_names=[\"labels\"]\n",
" )\n",
"\n",
")\n",
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},
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