diff --git "a/FineTuning_StableCode.ipynb" "b/FineTuning_StableCode.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning_StableCode.ipynb" @@ -0,0 +1,12072 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kH18jD5cR_Ks", + "outputId": "1ea2e9da-0bd4-4355-977c-a67a79542176" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m7.7 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[31m13.1 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[31m21.4 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[31m5.8 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[31m9.7 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[31m7.1 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[31m10.7 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": 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": "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-schemaLinking-min\"\n", + "prev_checkpoint = \"checkpoint-1000\"" + ] + }, + { + "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": [ + "spider_id=\"NESPED-GEN/spider_selector_schemaReduzido\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xT2iRdCN_MFH" + }, + "source": [ + "### Load Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388, + "referenced_widgets": [ + "0cc4e4cc05294802b64c3214618f4c9b", + "1c1112f317204b7994a89092128be076", + "06c17495d16d4d5cbf1c5ed7c9671c32", + "ac7ff8b042cc4179a490d5e65afbdee7", + "c7d2a4dc0f9f4f2a948edeb6905d0021", + "7cd9ddb882e042078622b7bd30998ebc", + "ac22c845dacb448cab1df18149e5b765", + "4362a9b620774edc9fcb97a8fecac118", + "605c8f6a397f4fadae15145ee482434f", + "c01a563b8e28407bbadab1f04538c675", + "7d1ad3a059e44ac580b89d89eaaa89b5", + "52604994b7444805943b5c62845b8613", + "c62304eb7b654b408e5e928240baf4cb", + "025e5af5a02a4e90ada9931e7fe64aa5", + "7ff01aaf56e14506a9ccf42c435ef1f9", + "306fa7239ae84b759ee7839cfbde0a75", + "056cdf9e78674043b7e598372c4b1327", + "208bc4af067f441dbe03d9ccd7377145", + "9cebf534f15848f9b83e9e6d8b6aefff", + "7e72a8ee32dd416ca936da9b68f0bc2e", + "dfe862ac162d42bbb478bc2e3b1ef6ca", + "2e5f033eee9e4f3b809df7263ede073c", + "4046d38d242449a6adba0c97f9bc8ebb", + "75943815057949b6b0e2c6963bc0c237", + "b25fe5934c874729895c7a2476d87a72", + "b3bc8461ce2e42e2a4ebcd049642d58b", + "a7784c4292494acf812f91a7a77b8cfa", + "03390578d4974180bb77d1fe547c7b4d", + "45416ae636b44014a2b3a314642478c5", + "aaf9771751294e5abca584ac4581e779", + "027a810c3e5d4197bc9d477fec815cf4", + "8b4f1d90157547a5a3ee5de36247a4f5", + "56464a69b54b4e62b77f38fa172e4db6", + "d8f984caae71439a8fc511dfedd9b13f", + "5aefcddf1d2a47008e670d84d3b39f50", + "79e586c8bd5d4b9284c269b5b679f5d4", + "de048ef0875c48c28a99ff90bc5d3f3a", + "97d953c315b740379d86849486e51ae0", + "cc868b8e698e498fa17ef5a8ba55b6fa", + "4dc617b65ebe4d6fa7b02db01633bf25", + "1525dd7f11ff40f6a5308176338bc215", + "d0a5b06093f94bc493112cfa3716b261", + "79acbb2e0c5b46f59955ea5cdfc538b5", + "db99476d0e204c168a762a94eb2e0559", + "9588f60a516245f0bb47219ff33e3fa9", + "3d7aa75665b7447e89e8584901633923", + "a39b344add0f4289b43c260ea86a095d", + "8f146af42e0f410ca37e1eed166407e1", + "457a47ee9b454d6f94417136425c28ce", + "1dc4486e51104ab2b42306e924d35a07", + "42f4cff2784c4d55a4bcc8cac631f705", + "321fadda273944ada121e7846adbee90", + "2f98e8c4cebc4df3a7bd94094faae17a", + "d9303cbe547b4d06b8465d52998e0905", + "b952c28200f94998832c8c1abb7ef7dc" + ] + }, + "id": "lLKgY40efdJo", + "outputId": "65f987e5-3f09-440d-e884-90789a364ec2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: 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%| | 0.00/980 [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/" + }, + "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": 10, + "metadata": { + "id": "opbdu1g6eMuq" + }, + "outputs": [], + "source": [ + "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", + " question = row['question_en']\n", + " schema = row['schema_SQLDatabase_min']\n", + " schema_linking = row['selector_correct'].lower()\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 = [\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\"```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": 11, + "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": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EFY_pTEteMut", + "outputId": "cef8c345-5422-4561-a048-4d293dbd6dba" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|im_start|>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.<|im_end|>\n", + "<|im_start|>user\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": 13, + "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": 14, + "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": 15, + "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": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "L0qqP5Y9PtRh", + "outputId": "d110ea47-d92b-46fc-b521-39585f3b4218" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 7 modules to quantize: ['o_proj', 'k_proj', 'gate_proj', 'v_proj', 'q_proj', 'down_proj', 'up_proj']\n" + ] + } + ], + "source": [ + "modules = find_all_linear_names(model)\n", + "print(f\"Found {len(modules)} modules to quantize: {modules}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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": 18, + "metadata": { + "id": "buh0o2P2jwbx" + }, + "outputs": [], + "source": [ + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 403, + "referenced_widgets": [ + "be21fddd34284c4bb3f403483b323e8c", + "cc2b580cb85648f3890b3f9f5ae78105", + "dbaeb4a87dd84e9581e15415e2ae5c7f", + "9c412d76ef9144638140987d8f715b73", + "bd26abbcc99649d0b9f022fec9e4d6b8", + "9b9254d99e2f416396b0b83b8f3c469e", + 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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" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/8569 [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining LossValidation Loss

" + ] + }, + "metadata": {} + } + ], + "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": { + "id": "i39Ov4dsGv77", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 69 + }, + "outputId": "17022643-a6b9-4acc-bba9-96c75fc8f61c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + 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