diff --git "a/FineTuning - StableCode-SchemaLinking.ipynb" "b/FineTuning - StableCode-SchemaLinking.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning - StableCode-SchemaLinking.ipynb" @@ -0,0 +1,7409 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 22207, + "status": "ok", + "timestamp": 1731936919321, + "user": { + "displayName": "HighSchoolMinas - MinasCoders", + "userId": "06806689059039259777" + }, + "user_tz": 180 + }, + "id": "kH18jD5cR_Ks", + "outputId": "924139c0-7305-4d4f-e4c2-e9cbf88b69cd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m5.3 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[31m8.9 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[31m21.3 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[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m10.6 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[31m7.9 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[31m7.2 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", + "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 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": null, + "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": "basaX_55Yf_D" + }, + "outputs": [], + "source": [ + "#transformers.logging.set_verbosity_info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bkkjgGdlrNcq" + }, + "outputs": [], + "source": [ + "WRITE_TOKEN = userdata.get('hf_write')\n", + "READ_TOKEN = userdata.get('hf_read')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7CKnwlRfZj4V" + }, + "outputs": [], + "source": [ + "model_name = \"stabilityai/stable-code-instruct-3b\"\n", + "out_name = \"lleticiasilvaa/StableCode-text2SQL-schemaReduzido\"\n", + "prev_checkpoint = None #\"checkpoint-500\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9zs7nCmt-pMC" + }, + "outputs": [], + "source": [ + "#!huggingface-cli login" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PZdnxs8k-Cgl" + }, + "outputs": [], + "source": [ + "spider_id=\"NESPED-GEN/spider_selector_schemaReduzido\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xT2iRdCN_MFH" + }, + "source": [ + "### Load Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 264, + "referenced_widgets": [ + "da5c091084d144b5bcf1c09c0dcde4f0", + "2259a5d56ca14424b4b83e4814d8ed5c", + "4f827e7e9c8143a9a66fbc5a337453c6", + "41db38eeaa224263a915868a767f7a7f", + "8a3c6a8ba4f4442487b588008d833250", + "4e7d6f28d53f4f5a9602bbeb97b18977", + "4eecfd540245491baa0eef44e55f8c5c", + "35a226337cd0484c9f1d16c2207c24a9", + "3f953a090a6e40cc9c170495d55477da", + "55282b1ef4634746853550df79492781", + "0b2336d126e84bc095a24971608f4187", + "c13e11608e7a461a942a575b54e764c7", + "d0d513bf8cad48b3bdd9870f39f36058", + "f020a3012eb2412683960caa0b0e8d22", + "a34b8d9700864ea3bce651705bfaebf5", + "3a24ff07ca894ac68f0f35bfeef09c31", + "bb483effb81248b99911546ddd11e0d7", + "918dfb6e0be647b896e4a707fe7a2c7c", + "b566d6281d4140efb609008c5cf16a34", + "6ef28a18e0a14ce59074c76b0fa46cea", + "61e892dcf80f40cd9e1c895c678a9c9d", + "553fad3cddd344dea8d52f3ab2a49f85", + "e4e00110363d4dddb9ba996c038743da", + "6f0b37bffb4e4fed990a0a9bf8b58829", + "714ceded01bf48118a6224048571a8e5", + "4b044760037a454192ed3220e1b92845", + "6347edc1ac59495b94700bce2cc8d802", + "e982e5da491843a5aecf0778f9f86552", + "d52845ae280040059faf6f46b64acbaa", + "42a6626eea084c44a3aa409e45d23592", + "1a99c1df706646d09b604d130cface07", + "82365e54a4ad44248642db67f854f9a7", + "cd4305d9e72c4350920592b70a5032f9", + "6769642dae9146ba87f576afa219f23b", + "ce39f86d765f4bf4a6be248ce6a91b53", + "7470bd376b2542f39be29048307b2f4b", + "78752f93c4f44cb2a3d4ba9f1a0f515c", + "46163d43293a449e969871a3380d6662", + "f2218f51cc49430b86f8447f143f14ee", + "90d871c75b4d409682fb7df71431f948", + "e54f383092c54422bba435911e5bc132", + "090c233031144c32a0c558f1a0f4d856", + "af07adf75b694c27a4ff47d2b94ada12", + "2475467d81314b969eabe7cbaa688215", + "71f55dee7ad244db921fa67a1ac3a9b9", + "e59c79d891db4489a03ed61569fc4c76", + "0f9c5ff272944cdc9b91a4c4a50d5d61", + "b9a2e6536ed14af0b1172057c18fa0ae", + "151e358e1c18478b8563e7b170512865", + "447ed2f1f5ad4800973ca622ed1d0d17", + "e8d33ddec7f949608ca80670656fe9de", + "92ebec4232bb49b3a2d89142596613d7", + "c60acfa5afef4c21b4ee82d17e354f3c", + "d9056a6ca2484d98b03b0fa9d8631158", + "10d1a188428248c1bc62ee11fe8131ce" + ] + }, + "executionInfo": { + "elapsed": 10362, + "status": "ok", + "timestamp": 1731936986600, + "user": { + "displayName": "HighSchoolMinas - MinasCoders", + "userId": "06806689059039259777" + }, + "user_tz": 180 + }, + "id": "lLKgY40efdJo", + "outputId": "b576abed-3fae-4e52-d094-ab270c9f8541" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "da5c091084d144b5bcf1c09c0dcde4f0", + "version_major": 2, + "version_minor": 0 + }, + "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\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IE87Vlt1hrje" + }, + "outputs": [], + "source": [ + "import re\n", + "\n", + "def replace_alias_with_table(query):\n", + " # Expressão regular para encontrar tabelas com alias, capturando o nome da tabela e o alias\n", + " alias_pattern = re.compile(r'(\\bFROM\\b|\\bJOIN\\b)\\s+(\\w+)\\s+AS\\s+(\\w+)', re.IGNORECASE)\n", + "\n", + " # Substituições de aliases encontrados no padrão\n", + " aliases = {match.group(3): match.group(2) for match in alias_pattern.finditer(query)}\n", + "\n", + " # Substituir cada alias pelo nome da tabela correspondente\n", + " for alias, table in aliases.items():\n", + " query = re.sub(r'\\b' + alias + r'\\b', table, query)\n", + "\n", + " # Remover 'AS' e alias das cláusulas 'FROM' e 'JOIN'\n", + " query = re.sub(r'\\bAS\\s+\\w+', '', query, flags=re.IGNORECASE)\n", + " return query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6RDDdVgP5gqT" + }, + "outputs": [], + "source": [ + "def to_sql(query):\n", + " return sqlparse.format(replace_alias_with_table(query), reindent=True, keyword_case='upper')\n", + "\n", + "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", + " question = row['question_en']\n", + " schema = row['schema_SQLDatabase_reduzido']\n", + " sql = to_sql(row['query'])\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", + " chat = [\n", + " {'role': 'system', 'content': system},\n", + " {'role': 'user', 'content': f\"# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n", + " {'role': 'assistant', 'content': f\"```sql\\n{sql}\\n```\\n\"}\n", + " ]\n", + "\n", + " # chat = [\n", + " # {\"role\": \"schema\", \"content\": schema},\n", + " # {\"role\": \"user\", \"content\": question},\n", + " # {\"role\": \"assistant\", \"content\": sql},\n", + " # ]\n", + "\n", + " row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n", + "\n", + " return row" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2trHEegL5gqU" + }, + "outputs": [], + "source": [ + "# spider_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/spider/spider_chain_of_thought.json\", \"r\"))\n", + "# bird_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/bird/bird_chain_of_thought.json\", \"r\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "N4jWrC7s5gqU" + }, + "outputs": [], + "source": [ + "# df['CoT'] = spider_chain + bird_chain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bTF0pBsw5gqU" + }, + "outputs": [], + "source": [ + "df = df.apply(apply_template, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "L4tjUv7o5gqV" + }, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DfJvLaGR5gqV" + }, + "outputs": [], + "source": [ + "# df['n_tokens'] = df['text'].apply(lambda x: len(tokenizer.encode(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vJseOHIu5gqW" + }, + "outputs": [], + "source": [ + "# import seaborn as sns\n", + "# sns.histplot(df['n_tokens'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 5, + "status": "ok", + "timestamp": 1731629325148, + "user": { + "displayName": "LLeticia_o@hotmail.com", + "userId": "07103443173840805964" + }, + "user_tz": 180 + }, + "id": "PIvSnr6Y5gqW", + "outputId": "2d3c87b0-eafc-487a-d563-8dc708283d32" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|im_start|>system\n", + "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.<|im_end|>\n", + "<|im_start|>user\n", + "# Schema:\n", + "```sql\n", + "CREATE TABLE city (\n", + " City_ID INT,\n", + " Status TEXT,\n", + " Population REAL,\n", + " PRIMARY KEY (City_ID)\n", + ");\n", + "```\n", + "\n", + "# Question: Show the status shared by cities with population bigger than 1500 and smaller than 500.<|im_end|>\n", + "<|im_start|>assistant\n", + "```sql\n", + "SELECT Status\n", + "FROM city\n", + "WHERE Population > 1500 INTERSECT\n", + " SELECT Status\n", + " FROM city WHERE Population < 500\n", + "```\n", + "<|im_end|>\n", + "\n" + ] + } + ], + "source": [ + "print(df['text'][df.index[50]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "roZzKNOj5gqW" + }, + "outputs": [], + "source": [ + "_df = pd.DataFrame(columns=['text'])\n", + "_df['text'] = df.sample(frac=1, random_state=14).reset_index(drop=True)['text']\n", + "_df = Dataset.from_pandas(_df)\n", + "_df = _df.train_test_split(test_size=0.01, shuffle=True, seed=14)\n", + "train_dataset, valid_dataset = _df[\"train\"], _df[\"test\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b6mjOblXeMup" + }, + "source": [ + "#### Chat Template - Schema Linking" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "opbdu1g6eMuq" + }, + "outputs": [], + "source": [ + "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", + " question = row['question_en']\n", + " schema = row['schema_SQLDatabase']\n", + " schema_linking = 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", + "execution_count": null, + "metadata": { + "id": "x_HHKJ9VeMur" + }, + "outputs": [], + "source": [ + "df = df.apply(apply_template, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oF0-4l8DeMus" + }, + "outputs": [], + "source": [ + "# df['n_tokens'] = df['text'].apply(lambda x: len(tokenizer.encode(x)))\n", + "# import seaborn as sns\n", + "# sns.histplot(df['n_tokens'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1731937378053, + "user": { + "displayName": "HighSchoolMinas - MinasCoders", + "userId": "06806689059039259777" + }, + "user_tz": 180 + }, + "id": "EFY_pTEteMut", + "outputId": "a71630ce-da91-4aac-e8ca-18886271f22d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|im_start|>system\n", + "You are a helpful assistant.<|im_end|>\n", + "<|im_start|>user\n", + "# System:\n", + "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", + "# Schema:\n", + "```sql\n", + "CREATE TABLE Addresses (\n", + " address_id INTEGER,\n", + " line_1 VARCHAR(80),\n", + " line_2 VARCHAR(80),\n", + " city VARCHAR(50),\n", + " zip_postcode CHAR(20),\n", + " state_province_county VARCHAR(50),\n", + " country VARCHAR(50),\n", + " PRIMARY KEY (address_id)\n", + ");\n", + "\n", + "CREATE TABLE People (\n", + " person_id INTEGER,\n", + " first_name VARCHAR(255),\n", + " middle_name VARCHAR(255),\n", + " last_name VARCHAR(255),\n", + " cell_mobile_number VARCHAR(40),\n", + " email_address VARCHAR(40),\n", + " login_name VARCHAR(40),\n", + " password VARCHAR(40),\n", + " PRIMARY KEY (person_id)\n", + ");\n", + "\n", + "CREATE TABLE Students (\n", + " student_id INTEGER,\n", + " student_details VARCHAR(255),\n", + " PRIMARY KEY (student_id),\n", + " FOREIGN KEY (student_id) REFERENCES People(person_id)\n", + ");\n", + "\n", + "CREATE TABLE Courses (\n", + " course_id VARCHAR(100),\n", + " course_name VARCHAR(120),\n", + " course_description VARCHAR(255),\n", + " other_details VARCHAR(255),\n", + " PRIMARY KEY (course_id)\n", + ");\n", + "\n", + "CREATE TABLE People_Addresses (\n", + " person_address_id INTEGER,\n", + " person_id INTEGER,\n", + " address_id INTEGER,\n", + " date_from DATETIME,\n", + " date_to DATETIME,\n", + " PRIMARY KEY (person_address_id),\n", + " FOREIGN KEY (address_id) REFERENCES Addresses(address_id),\n", + " FOREIGN KEY (person_id) REFERENCES People(person_id)\n", + ");\n", + "\n", + "CREATE TABLE Student_Course_Registrations (\n", + " student_id INTEGER,\n", + " course_id INTEGER,\n", + " registration_date DATETIME,\n", + " PRIMARY KEY (student_id),\n", + " FOREIGN KEY (course_id) REFERENCES Courses(course_id),\n", + " FOREIGN KEY (student_id) REFERENCES Students(student_id)\n", + ");\n", + "\n", + "CREATE TABLE Student_Course_Attendance (\n", + " student_id INTEGER,\n", + " course_id INTEGER,\n", + " date_of_attendance DATETIME,\n", + " PRIMARY KEY (student_id),\n", + " FOREIGN KEY (student_id) REFERENCES Student_Course_Registrations(student_id),\n", + " FOREIGN KEY (course_id) REFERENCES Student_Course_Registrations(course_id)\n", + ");\n", + "\n", + "CREATE TABLE Candidates (\n", + " candidate_id INTEGER,\n", + " candidate_details VARCHAR(255),\n", + " PRIMARY KEY (candidate_id),\n", + " FOREIGN KEY (candidate_id) REFERENCES People(person_id)\n", + ");\n", + "\n", + "CREATE TABLE Candidate_Assessments (\n", + " candidate_id INTEGER,\n", + " qualification CHAR(15),\n", + " assessment_date DATETIME,\n", + " asessment_outcome_code CHAR(15),\n", + " PRIMARY KEY (candidate_id),\n", + " FOREIGN KEY (candidate_id) REFERENCES Candidates(candidate_id)\n", + ");\n", + "```\n", + "\n", + "# Question: How many students are attending English courses?<|im_end|>\n", + "<|im_start|>assistant\n", + "```json\n", + "{\n", + " 'Courses': ['course_id', 'course_name'],\n", + " 'Student_Course_Attendance': ['student_id', 'course_id']\n", + "}\n", + "```<|im_end|>\n", + "\n" + ] + } + ], + "source": [ + "print(df['text'][df.index[70]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "puYY-BqFeMuu" + }, + "outputs": [], + "source": [ + "_df = pd.DataFrame(columns=['text'])\n", + "_df['text'] = df.sample(frac=1, random_state=14).reset_index(drop=True)['text']\n", + "_df = Dataset.from_pandas(_df)\n", + "_df = _df.train_test_split(test_size=0.01, shuffle=True, seed=14)\n", + "train_dataset, valid_dataset = _df[\"train\"], _df[\"test\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DWpXeuO_KlLS" + }, + "source": [ + "### Finetuning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0oVpZDj1AXY9" + }, + "outputs": [], + "source": [ + "from huggingface_hub import login, create_repo\n", + "from google.colab import userdata\n", + "import wandb\n", + "import os\n", + "\n", + "#token = userdata.get('hf_write')\n", + "token = WRITE_TOKEN\n", + "login(token=token)\n", + "set_seed(1234)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KRhO7UJ-Q4Y8" + }, + "outputs": [], + "source": [ + "def find_all_linear_names(model, new_tokens=False):\n", + " lora_module_names = set()\n", + " for name, module in model.named_modules():\n", + " if isinstance(module, bnb.nn.Linear4bit) or isinstance(module, bnb.nn.Linear8bitLt):\n", + " names = name.split(\".\")\n", + " lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n", + " if(new_tokens):\n", + " lora_module_names.add(\"lm_head\")\n", + " return list(lora_module_names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 503, + "status": "ok", + "timestamp": 1731937441903, + "user": { + "displayName": "HighSchoolMinas - MinasCoders", + "userId": "06806689059039259777" + }, + "user_tz": 180 + }, + "id": "L0qqP5Y9PtRh", + "outputId": "4d415c71-e1cb-4bf1-ef73-d6d76cd35d44" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7 modules to quantize: ['up_proj', 'v_proj', 'q_proj', 'k_proj', 'o_proj', 'gate_proj', 'down_proj']\n" + ] + } + ], + "source": [ + "modules = find_all_linear_names(model)\n", + "print(f\"Found {len(modules)} modules to quantize: {modules}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uFUnJrbjPwAT" + }, + "outputs": [], + "source": [ + "peft_config = LoraConfig(\n", + " lora_alpha=128, #primeira versão = 16\n", + " lora_dropout=0.1,\n", + " r=64,\n", + " # bias=\"none\",\n", + " # task_type=\"CAUSAL_LM\",\n", + " target_modules=modules,\n", + " # modules_to_save=[\"embed_tokens\"], #quando adicionar tokens speciais\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "buh0o2P2jwbx" + }, + "outputs": [], + "source": [ + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true, + "base_uri": "https://localhost:8080/", + "height": 736, + "referenced_widgets": [ + "13509555b5c3489dbfbe64f2f6f284df", + "1ac85930f80f46f0bace12efed6ef9cb", + "80d438af0fb44b479f1a2ccfa15f27fc", + "7833338232914b5bb8144c8844d4b4e5", + "7bd5866d6064425e8d4604279f47ad7a", + "a3802f89dba94a3697d534522a2bb786", + "2afb99816a8e4029966f3b84c7792931", + "0ea6db73f2444a7d92e39d8b3725a954", + "06888bf0c7ac4d9fbde3b2e66de722fa", + "e8e7905f02ec444ea5c10d4b3846534a", + "0d690a90e84e41cbb12e37301e8adadb", + "2fe54466218640b3acaefe02890bffbd", + "41360284ac634c8081df2b01449c2ca7", + "7faceca502ed4af091819f5b15688569", + "c63a4ad82fa946a3be5e39f61c5faba4", + "2b69aa1e7f5f4c6d9995ccdbbe7e8238", + "195abb3ae03744bebacbfd2dbe874e19", + "535755d5b72d4ca49e469007e1663101", + "fa38563f9546433597730c810187afbd", + "80a51b467dda4e6293fd2e560badaa29", + "93498cdfa1f248b98245086dc49cbfa1", + "cf196d5297fa4e6d91a59729eeb2ef16" + ] + }, + "id": "9bD7ea0F-GQn", + "outputId": "1392d82f-e296-49ca-ea27-50030d6adb5c" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': dataset_text_field, max_seq_length. Will not be supported from version '0.13.0'.\n", + "\n", + "Deprecated positional argument(s) used in SFTTrainer, please use the SFTConfig to set these arguments instead.\n", + " warnings.warn(message, FutureWarning)\n", + "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:300: UserWarning: You passed a `max_seq_length` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:328: UserWarning: You passed a `dataset_text_field` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "13509555b5c3489dbfbe64f2f6f284df", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/8569 [00:00\n", + " \n", + " \n", + " [1071/1071 3:44:53, Epoch 0/1]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "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:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 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", + "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:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 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", + "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:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 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", + "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:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 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" + ] + } + ], + "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", + " logging_dir=\"out/logs\",\n", + "\n", + " eval_strategy=strategy,\n", + " eval_steps=steps,\n", + "\n", + " 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", + "if(prev_checkpoint != None):\n", + " trainer.train(f\"out/{prev_checkpoint}\")\n", + "else:\n", + " trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 69 + }, + "executionInfo": { + "elapsed": 15690, + "status": "ok", + "timestamp": 1731951109474, + "user": { + "displayName": "HighSchoolMinas - MinasCoders", + "userId": "06806689059039259777" + }, + "user_tz": 180 + }, + "id": "i39Ov4dsGv77", + "outputId": "b93f69d9-88e4-4fbd-aa6f-54e92b2045c7" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "CommitInfo(commit_url='https://huggingface.co/lleticiasilvaa/StableCode-text2SQL-schemaReduzido/commit/0fc20c6f5aa398879ac803f29dc12c0a2e654af1', commit_message='End of training', commit_description='', oid='0fc20c6f5aa398879ac803f29dc12c0a2e654af1', pr_url=None, repo_url=RepoUrl('https://huggingface.co/lleticiasilvaa/StableCode-text2SQL-schemaReduzido', endpoint='https://huggingface.co', repo_type='model', repo_id='lleticiasilvaa/StableCode-text2SQL-schemaReduzido'), pr_revision=None, pr_num=None)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer.push_to_hub()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + 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