diff --git "a/FineTuning_Llama_3_2.ipynb" "b/FineTuning_Llama_3_2.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning_Llama_3_2.ipynb" @@ -0,0 +1,13572 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kH18jD5cR_Ks", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bce41acd-1822-4357-bf94-207bccc94c35" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.1/44.1 kB\u001b[0m \u001b[31m1.7 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[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m310.2/310.2 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m8.0/10.0 MB\u001b[0m \u001b[31m49.1 MB/s\u001b[0m eta \u001b[36m0:00:01\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" + }, + "outputs": [], + "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')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "7CKnwlRfZj4V" + }, + "outputs": [], + "source": [ + "model_name = \"meta-llama/Llama-3.2-1B-Instruct\"\n", + "out_name = \"lleticiasilvaa/Llama-3.2-text2SQL-v0\"\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": [ + "bird_id=\"NESPED-GEN/bird\"\n", + "spider_id=\"NESPED-GEN/spider_variacoes_formato_schema\"\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": 371, + "referenced_widgets": [ + "d06ccc6c336f4608b697a286d46d5bc0", + "86beaa2783ac47a1898bc30e2d85d408", + "dd1f129642d64591a34a5877028256f7", + "6b3df8405e2f458eacb247e1d972bd7e", + "7e64ea7cf46d48699d9e9b2edd37e897", + "7c9126ecca1b46bd9bff15f56b211ac8", + "1040cd57acec435b85b947f20823bf9d", + "2a0bafbc06bc42f0860c06b9c264f407", + "aecc1109985b4c759a55c7cd6e77baba", + "10976bf0bb204ccfbca4682e97f95fc9", + "9765380f41fc42bcb421154b7cd6f143", + "fe88169757ee4fcdb1367002c48cc89c", + "5cb47e3b8eef41f689ca45409183b619", + "e0ddebb6bc9d4516940702fd42f68719", + "fb1c79e073b44cec86bec8272e803cee", + "5c80eb47245748639c6c8b6c03368aab", + "ba3ae28099f9464d8b50ba9ea39a3de7", + "270d2885bd50486fa8aab95958d9d1f8", + "e13cbc36505749fea70c82f5b52a2c42", + "874de6e1a3f74e35ba1f58f8f712d503", + "e983b7f9cdaa4daab78535a9aeeb05a9", + "8c93b9f940904586ba24e511a89da7af", + "927df12e18bb4906b78420adfd682438", + "e75f4b8c148d4fbd8f1bbd269d17e254", + "cb8f3414a3364a889c5ca251aa56279a", + "e94c5fb4fd8f4fab91baa042eccb7fcd", + "d9b2d60ff3d44434ae9bc9f220033e0b", + "4ee4455fd7774fcfb788f564cc0ef331", + "b1acd73c6d13426e8baab6259ce36575", + "be2a90c7134b4bbf832b114e800ef03f", + "0554703899164a0381a96800f46f6b53", + "f1df9f4b029a4f15b57902dd57d6e5ae", + "40a7f400976841b58aa18a03181e915f", + "f16bcc9871ed439aae526755e77298b2", + "d0f13597952542e3a7873ef8f0381e82", + "efc9ef1dd9a9475abece99238c02bac1", + "c444eada62a04e948ab8a330055294af", + "7e1e2e67c0a44557913ebd77da3fbfd4", + "ba2a411468204f10b2646d5935d8e852", + "fa72962ac1a543a3962891ee1e24f11a", + "158ce1f6476a406a8012ff3ac66ca24d", + "e6f0f0203ca64788a4c5838d73ca4a65", + "35821731a47747a1bd74341d0da14811", + "938d371800524cd3838801cba072867f", + "4eff3a2b62574619a3f7844b57b2f0f6", + "2aa7915d08394088ad5efe8acdff39da", + "f2a6c8d31aad463393e4d2b238fa3d08", + "71333a8184644192b486dd89e14a8d84", + "7b2599f5eb5f47669c76829fc0c2e96c", + "d697573fd28846db9ab254df6715c793", + "e1da81d929a344d28f1f2868237e6d67", + "15f13104853446528b09f28e447509c7", + "178d4319c203437e901236347212c3d2", + "5b12e0842d0f437184113001bc1ec6ee", + "ad8d4c930c594f68b5ab961adf68e796" + ] + }, + "outputId": "1e78620c-4cfe-443b-850e-be62d641d88f" + }, + "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%| | 0.00/737 [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "d06ccc6c336f4608b697a286d46d5bc0" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "dev-00000-of-00001.parquet: 0%| | 0.00/354k [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "fe88169757ee4fcdb1367002c48cc89c" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "train-00000-of-00001.parquet: 0%| | 0.00/2.50M [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "927df12e18bb4906b78420adfd682438" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating dev split: 0%| | 0/1034 [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f16bcc9871ed439aae526755e77298b2" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating train split: 0%| | 0/8656 [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "4eff3a2b62574619a3f7844b57b2f0f6" + } + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Dataset({\n", + " features: ['db_id', 'question_en', 'query', 'answer', 'hardness', 'query_llm', 'schema_SQLDatabase', 'schema_SQLDatabase_min', 'schema_our', 'schema_our_min'],\n", + " num_rows: 8656\n", + "})" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "# bird = load_dataset(bird_id, split=\"train\").to_pandas()\n", + "spider = load_dataset(spider_id, split=\"train\")\n", + "spider" + ] + }, + { + "cell_type": "code", + "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" + ], + "metadata": { + "id": "X7cgEDOQOgaV" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "count = 0\n", + "for i in spider:\n", + " if 'AS ' in i['query_llm']:\n", + " count += 1\n", + " print(count)\n", + " print('--------------------------')\n", + " print('query = ', i['query'])\n", + " print('--------------------------')\n", + " print('query_llm = ', i['query_llm'])\n", + " print('--------------------------')\n", + " print(replace_alias_with_table(i['query']))\n", + " print('--------------------------')\n", + "print(count)" + ], + "metadata": { + "id": "mtY2zDbGO0H-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "ZEPwj93KPJsK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "D3d_091Ud0BG" + }, + "outputs": [], + "source": [ + "# df = pd.concat([spider, bird], axis=0)\n", + "# df = df.fillna('')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "niRTjA1tQIWr" + }, + "outputs": [], + "source": [ + "# df.reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "code", + "source": [ + "df = spider.to_pandas()" + ], + "metadata": { + "id": "po1gNdLjFKdZ" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#df = load_dataset(selector_id, split=\"trainSpider\").to_pandas()" + ], + "metadata": { + "id": "LeL7EfmQVyBt", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241, + "referenced_widgets": [ + "5a1e71143dc741efac418bc21eb99526", + "e380a31a7c9a41f1bd9de22a4eec9afd", + "73f947fba7da4fef829ee2034171a836", + "a9abf81190a54b29a4fbf7c2514d8b3e", + "68dd1789b9a34b91a74c7d00a6c5c491", + "dce14eebcd734217be5fd8bfd1099439", + "0ecff7b9301b4713870b0747682533b1", + "fb996e2abfcc4a0caaec2a2f3ff9fc9a", + "11a036e18eac4d7d809a05f68cfb1096", + "d10ad1893e1f48849896994284318067", + "418852eab70342b89b6c1589874d71d9", + "dc51fa9dd4bc4047b0bb22500b95b1f7", + "d01ce95ef53d49bd913fcec32505729d", + "748a1613620249e6ae4d71aee744024f", + "f1c7ee8b443c4638a0d1423248a2207b", + "01301a7d24a3430387ccd7997ce62361", + "3bfbe04029a544c0924738e19e7a9436", + "f3b80afa9a464264b4a001b9eae61983", + 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"2b9ace70a9bc401085fdbc8cced20d38", + "358831d17c974c2c95648cf9ee7c9294", + "8bb4561e208b4d2f823c782124cfa335", + "1a01eb10b70e42a58ebfedb6ccd61a73", + "6880557c5da649c19c855735754906d1", + "9dd0e981b51648d888fb724c936b6cf4", + "9c1bce42a2494449b8f3a833ef09e862", + "5be7663a62f44df9ac01171e421c732c", + "9611ef2940f74dbab86496d99bf4d7eb", + "1e6881df003345718b84ad9ed160a8af", + "e03975280a21420d8880f8bfd93ac44a", + "0b37dc50f0cd4f149feb3f7ce08f2630", + "0abf618ec97e4ca8873c8af349516150", + "c9bd191293104538bdbc0a5539be4d0a", + "f3fe35e2b0164044aed92f8f776a17b5", + "3a9a0de33597425ab7e550ec32aad186", + "f6357593f5f64966a4f115adbcbec940", + "f283bc804ab348a29c33a82368552fd5", + "3248d30b29f84bdcbe4c43594c803673", + "307a99fb69894c4cac341e23119150f2", + "46d219bf79384b1d98052febd0271589", + "71990f86fca248bd8d1e75be403d70db", + "9793ebb3046b4e0cadc483222c6d88f9", + "4a47677ae2204165be1c7a27214afab4", + "9c1c3f497c874132b43c6c8e86bbf3e1", + "814ec31670f543e798527a887db56ea0", + "e022ce00981049349403502e2a07758b", + "aad2ce00485e4468a787dbca24d0ba04", + "03994d990e7442b5ab2249403fa62467", + "c505d4d70cfe4c23844c7fef29b79c16", + "d88bd0e5dc454761bd9db02488746996", + "9a3733ca2451421e9ad5613f94ea7d89", + "48a9e7a72ccb4352a7393a2461716e99" + ] + }, + "outputId": "4f3f46d3-c400-4dcb-b2ab-acc7880e3e90" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/1.23k [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "5a1e71143dc741efac418bc21eb99526" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "trainSpider-00000-of-00001.parquet: 0%| | 0.00/2.00M [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "dc51fa9dd4bc4047b0bb22500b95b1f7" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "devSpider-00000-of-00001.parquet: 0%| | 0.00/297k [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "a5f7375c80e941418802baba0868b509" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "testSpider-00000-of-00001.parquet: 0%| | 0.00/649k [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "25e2b5a94982477ea03255387e1a4c95" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating trainSpider split: 0%| | 0/8656 [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "2b9ace70a9bc401085fdbc8cced20d38" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating devSpider split: 0%| | 0/1034 [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "0b37dc50f0cd4f149feb3f7ce08f2630" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating testSpider split: 0%| | 0/2147 [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "9793ebb3046b4e0cadc483222c6d88f9" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ON-hPsEYM1Bu" + }, + "source": [ + "# Load Base Model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "yEAZpfzlNOHW" + }, + "outputs": [], + "source": [ + "def download_checkpoint(adapter_model_id, checkpoint):\n", + " fs = HfFileSystem()\n", + " for file in fs.ls(f'{adapter_model_id}/{checkpoint}', detail=False):\n", + " file_name = file.split(checkpoint)[-1]\n", + "\n", + " hf_hub_download(repo_id=adapter_model_id, filename=(f'{checkpoint}{file_name}'), local_dir='out')\n", + "\n", + " for file in fs.ls(f'{adapter_model_id}/logs', detail=False):\n", + " file_name = file.split(checkpoint)[-1]\n", + "\n", + " hf_hub_download(repo_id=adapter_model_id, filename=(f'logs/{file_name.split(\"/\")[-1]}'), local_dir='out')" + ] + }, + { + "cell_type": "code", + "source": [ + "# download_checkpoint(out_name, prev_checkpoint)" + ], + "metadata": { + "id": "enUxjGXEqHxg" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "M7DoqQMlM_nW", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 593, + "referenced_widgets": [ + "d53540d5a60e4eabadb2549219360741", + "ff68088fb821414687337ce14777185f", + "ea1cb46a7d9d4588a725b1952737e860", + "0794a6712ab74914a3e9aeb7ce41ff34", + "d33566563ffe4549ac9a3a5cd700a35d", + 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"source": [ + "seed=14\n", + "\n", + "\n", + "if (prev_checkpoint != None):\n", + " try:\n", + " download_checkpoint(out_name, prev_checkpoint)\n", + " except:\n", + " pass\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, token=READ_TOKEN, map_device=\"auto\", add_eos_token=True, use_fast=True)\n", + "\n", + "new_tokens = {'additional_special_tokens': ['[SQL]','[/SQL]', '[QUESTION]','[/QUESTION]']}\n", + "#adicionar tokens especiais:\n", + "# if (prev_checkpoint == None):\n", + "# tokenizer.add_special_tokens(new_tokens)\n", + "\n", + "\n", + "if torch.cuda.is_bf16_supported():\n", + " compute_dtype = torch.bfloat16\n", + " attn_implementation = 'flash_attention_2'\n", + "else:\n", + " compute_dtype = torch.float16\n", + " attn_implementation = 'sdpa'\n", + "\n", + "tokenizer.pad_token = tokenizer.eos_token\n", + "tokenizer.padding_side = \"right\"\n", + "\n", + "\n", + "bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=compute_dtype,\n", + " bnb_4bit_use_double_quant=False,\n", + ")\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + " torch_dtype=compute_dtype,\n", + " device_map=\"auto\",\n", + " quantization_config=bnb_config,\n", + "\n", + " trust_remote_code=True,\n", + " token=READ_TOKEN,\n", + " # attn_implementation=attn_implementation\n", + ")\n", + "\n", + "# se adicionar special_tokens tem que fazer resize do tokenizer:\n", + "# model.resize_token_embeddings(len(tokenizer))\n", + "\n", + "## model.resize_token_embeddings(max(len(tokenizer), model.config.vocab_size))" + ] + }, + { + "cell_type": "code", + "source": [ + "# se adicionar special_tokens tem que fazer resize do tokenizer:\n", + "#model.resize_token_embeddings(len(tokenizer))\n", + "\n", + "#model.resize_token_embeddings(max(len(tokenizer), model.config.vocab_size))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fLuqzhSJBvi8", + "outputId": "65293049-fcc8-491b-aaca-579bca0686c7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "The new embeddings will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`\n", + "The new lm_head weights will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Embedding(32004, 2048)" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "leGEqxURAL4r" + }, + "source": [ + "#### Chat Template - Schema Linking" + ] + }, + { + "cell_type": "code", + "source": [ + "# tokenizer.chat_template = \"\"\"\n", + "# {% if messages[0]['role'] == 'system' %}\n", + "# {% set loop_messages = messages[1:] %}\n", + "# {% set system_message = messages[0]['content'] %}\n", + "# {% else %}\n", + "# {% set loop_messages = messages %}\n", + "# {% set system_message = 'You are an SQL expert tasked with answering questions about an SQLite database./n/Your responses must always consist of valid SQL code and only that.' %}\n", + "# {% endif %}\n", + "# {{ '# <|system|>/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", + "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_t \\\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_t \\\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_t \\\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", + " text \n", + "0 <|begin_of_text|><|start_header_id|>system<|en... \n", + "1 <|begin_of_text|><|start_header_id|>system<|en... \n", + "2 <|begin_of_text|><|start_header_id|>system<|en... \n", + "3 <|begin_of_text|><|start_header_id|>system<|en... \n", + "4 <|begin_of_text|><|start_header_id|>system<|en... " + ], + "text/html": [ + "\n", + "
\n", + " | db_id | \n", + "schema | \n", + "question_en | \n", + "hardness | \n", + "schema_llm | \n", + "query_llm | \n", + "selector | \n", + "schema_llm_ct | \n", + "schema_llm_columns_min | \n", + "schema_llm_columns_min_ct | \n", + "schema_llm_all_min | \n", + "schema_llm_all_min_ct | \n", + "schema_dict | \n", + "selector_correct | \n", + "schema_llm_t | \n", + "schema_llm_columns_min_t | \n", + "schema_llm_all_min_t | \n", + "text | \n", + "
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0 | \n", + "department_management | \n", + "\\nCREATE TABLE department (\\n\\t\"Department_ID\"... | \n", + "How many heads of the departments are older th... | \n", + "easy | \n", + "Table department (\\n department.Department_... | \n", + "SELECT count(*) FROM head WHERE age > 56 | \n", + "{\\n 'head': ['head_id', 'age']\\n} | \n", + "CREATE TABLE department (\\n department.Depa... | \n", + "Table department (\\n department.department_... | \n", + "CREATE TABLE department (\\n department.depa... | \n", + "Table department (\\n department.department_... | \n", + "CREATE TABLE department (\\n department.depa... | \n", + "{\\n 'department': ['Department_ID', 'Name', '... | \n", + "{\\n 'head': ['head_ID', 'age']\\n} | \n", + "TABLE department (\\n department.Department_... | \n", + "TABLE department (\\n department.department_... | \n", + "TABLE department (\\n department.department_... | \n", + "<|begin_of_text|><|start_header_id|>system<|en... | \n", + "
1 | \n", + "department_management | \n", + "\\nCREATE TABLE department (\\n\\t\"Department_ID\"... | \n", + "List the name, born state and age of the heads... | \n", + "medium | \n", + "Table department (\\n department.Department_... | \n", + "SELECT name , born_state , age FROM head ORD... | \n", + "{\\n 'head': ['head_id', 'name', 'born_state',... | \n", + "CREATE TABLE department (\\n department.Depa... | \n", + "Table department (\\n department.department_... | \n", + "CREATE TABLE department (\\n department.depa... | \n", + "Table department (\\n department.department_... | \n", + "CREATE TABLE department (\\n department.depa... | \n", + "{\\n 'department': ['Department_ID', 'Name', '... | \n", + "{\\n 'head': ['head_ID', 'name', 'born_state',... | \n", + "TABLE department (\\n department.Department_... | \n", + "TABLE department (\\n department.department_... | \n", + "TABLE department (\\n department.department_... | \n", + "<|begin_of_text|><|start_header_id|>system<|en... | \n", + "
2 | \n", + "department_management | \n", + "\\nCREATE TABLE department (\\n\\t\"Department_ID\"... | \n", + "List the creation year, name and budget of eac... | \n", + "medium | \n", + "Table department (\\n department.Department_... | \n", + "SELECT creation , name , budget_in_billions ... | \n", + "{\\n 'department': ['department_id', 'name', '... | \n", + "CREATE TABLE department (\\n department.Depa... | \n", + "Table department (\\n department.department_... | \n", + "CREATE TABLE department (\\n department.depa... | \n", + "Table department (\\n department.department_... | \n", + "CREATE TABLE department (\\n department.depa... | \n", + "{\\n 'department': ['Department_ID', 'Name', '... | \n", + "{\\n 'department': ['Department_ID', 'Name', '... | \n", + "TABLE department (\\n department.Department_... | \n", + "TABLE department (\\n department.department_... | \n", + "TABLE department (\\n department.department_... | \n", + "<|begin_of_text|><|start_header_id|>system<|en... | \n", + "
3 | \n", + "department_management | \n", + "\\nCREATE TABLE department (\\n\\t\"Department_ID\"... | \n", + "What are the maximum and minimum budget of the... | \n", + "medium | \n", + "Table department (\\n department.Department_... | \n", + "SELECT max(budget_in_billions) , min(budget_i... | \n", + "{\\n 'department': ['department_id', 'budget_i... | \n", + "CREATE TABLE department (\\n department.Depa... | \n", + "Table department (\\n department.department_... | \n", + "CREATE TABLE department (\\n department.depa... | \n", + "Table department (\\n department.department_... | \n", + "CREATE TABLE department (\\n department.depa... | \n", + "{\\n 'department': ['Department_ID', 'Name', '... | \n", + "{\\n 'department': ['Department_ID', 'Budget_i... | \n", + "TABLE department (\\n department.Department_... | \n", + "TABLE department (\\n department.department_... | \n", + "TABLE department (\\n department.department_... | \n", + "<|begin_of_text|><|start_header_id|>system<|en... | \n", + "
4 | \n", + "department_management | \n", + "\\nCREATE TABLE department (\\n\\t\"Department_ID\"... | \n", + "What is the average number of employees of the... | \n", + "easy | \n", + "Table department (\\n department.Department_... | \n", + "SELECT avg(num_employees) FROM department WHER... | \n", + "{\\n 'department': ['department_id', 'ranking'... | \n", + "CREATE TABLE department (\\n department.Depa... | \n", + "Table department (\\n department.department_... | \n", + "CREATE TABLE department (\\n department.depa... | \n", + "Table department (\\n department.department_... | \n", + "CREATE TABLE department (\\n department.depa... | \n", + "{\\n 'department': ['Department_ID', 'Name', '... | \n", + "{\\n 'department': ['Department_ID', 'Ranking'... | \n", + "TABLE department (\\n department.Department_... | \n", + "TABLE department (\\n department.department_... | \n", + "TABLE department (\\n department.department_... | \n", + "<|begin_of_text|><|start_header_id|>system<|en... | \n", + "
Step | \n", + "Training Loss | \n", + "Validation Loss | \n", + "
---|
" + ] + }, + "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": 22, + "metadata": { + "id": "i39Ov4dsGv77", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "outputId": "d90ea9ba-385a-4693-94ac-4cd9db957ac8" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + 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