train_llm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from uuid import uuid4\n",
    "import pandas as pd\n",
    "\n",
    "from datasets import load_dataset\n",
    "import subprocess\n",
    "from transformers import AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from dotenv import load_dotenv,find_dotenv\n",
    "# load_dotenv(find_dotenv(),override=True)\n",
    "\n",
    "def max_token_len(dataset):\n",
    "    max_seq_length = 0\n",
    "    for row in dataset:\n",
    "        tokens = len(tokenizer(row['text'])['input_ids'])\n",
    "        if tokens > max_seq_length:\n",
    "            max_seq_length = tokens\n",
    "    return max_seq_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Max Length: 1000000000000000019884624838656\n"
     ]
    }
   ],
   "source": [
    "# model_name='TinyLlama/TinyLlama-1.1B-Chat-v0.1'\n",
    "model_name = 'mistralai/Mistral-7B-v0.1'\n",
    "# model_name = 'distilbert-base-uncased'\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model_max_length = tokenizer.model_max_length\n",
    "print(\"Model Max Length:\", model_max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max token length train: 1121\n",
      "Max token length validation: 38\n",
      "Block size: 2242\n"
     ]
    }
   ],
   "source": [
    "# Load dataset\n",
    "dataset_name = 'ai-aerospace/ams_data_train_generic_v0.1_100'\n",
    "dataset=load_dataset(dataset_name)\n",
    "\n",
    "# Write dataset files into data directory\n",
    "data_directory = './fine_tune_data/'\n",
    "\n",
    "# Create the data directory if it doesn't exist\n",
    "os.makedirs(data_directory, exist_ok=True)\n",
    "\n",
    "# Write the train data to a CSV file\n",
    "train_data='train_data'\n",
    "train_filename = os.path.join(data_directory, train_data)\n",
    "dataset['train'].to_pandas().to_csv(train_filename+'.csv', columns=['text'], index=False)\n",
    "max_token_length_train=max_token_len(dataset['train'])\n",
    "print('Max token length train: '+str(max_token_length_train))\n",
    "\n",
    "# Write the validation data to a CSV file\n",
    "validation_data='validation_data'\n",
    "validation_filename = os.path.join(data_directory, validation_data)\n",
    "dataset['validation'].to_pandas().to_csv(validation_filename+'.csv', columns=['text'], index=False)\n",
    "max_token_length_validation=max_token_len(dataset['validation'])\n",
    "print('Max token length validation: '+str(max_token_length_validation))\n",
    "      \n",
    "max_token_length=max(max_token_length_train,max_token_length_validation)\n",
    "if max_token_length > model_max_length:\n",
    "    raise ValueError(\"Maximum token length exceeds model limits.\")\n",
    "block_size=2*max_token_length\n",
    "print('Block size: '+str(block_size))\n",
    "\n",
    "# Define project parameters\n",
    "username='ai-aerospace'\n",
    "project_name='./llms/'+'ams_data_train-100_'+str(uuid4())\n",
    "repo_name='ams-data-train-100-'+str(uuid4())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'project_name': './llms/ams_data_train-100_6abb23dc-cb9d-428e-9079-e47deee0edd9', 'model_name': 'mistralai/Mistral-7B-v0.1', 'repo_id': 'ai-aerospace/ams-data-train-100-4601c8c8-0903-4f18-a6e8-1d2a40a697ce', 'train_data': 'train_data', 'validation_data': 'validation_data', 'data_directory': './fine_tune_data/', 'block_size': 2242, 'model_max_length': 1121, 'logging_steps': -1, 'evaluation_strategy': 'epoch', 'save_total_limit': 1, 'save_strategy': 'epoch', 'mixed_precision': 'fp16', 'lr': 3e-05, 'epochs': 3, 'batch_size': 2, 'warmup_ratio': 0.1, 'gradient_accumulation': 1, 'optimizer': 'adamw_torch', 'scheduler': 'linear', 'weight_decay': 0, 'max_grad_norm': 1, 'seed': 42, 'quantization': 'int4', 'lora_r': 16, 'lora_alpha': 32, 'lora_dropout': 0.05}\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "This set of parameters runs on a low memory gpu on hugging face spaces:\n",
    "{\n",
    "  \"block_size\": 1024,\n",
    "  \"model_max_length\": 2048,\n",
    "  x\"use_flash_attention_2\": false,\n",
    "  x\"disable_gradient_checkpointing\": false,\n",
    "  \"logging_steps\": -1,\n",
    "  \"evaluation_strategy\": \"epoch\",\n",
    "  \"save_total_limit\": 1,\n",
    "  \"save_strategy\": \"epoch\",\n",
    "  x\"auto_find_batch_size\": false,\n",
    "  \"mixed_precision\": \"fp16\",\n",
    "  \"lr\": 0.00003,\n",
    "  \"epochs\": 3,\n",
    "  \"batch_size\": 2,\n",
    "  \"warmup_ratio\": 0.1,\n",
    "  \"gradient_accumulation\": 1,\n",
    "  \"optimizer\": \"adamw_torch\",\n",
    "  \"scheduler\": \"linear\",\n",
    "  \"weight_decay\": 0,\n",
    "  \"max_grad_norm\": 1,\n",
    "  \"seed\": 42,\n",
    "  \"apply_chat_template\": false,\n",
    "  \"quantization\": \"int4\",\n",
    "  \"target_modules\": \"\",\n",
    "  x\"merge_adapter\": false,\n",
    "  \"peft\": true,\n",
    "  \"lora_r\": 16,\n",
    "  \"lora_alpha\": 32,\n",
    "  \"lora_dropout\": 0.05\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "model_params={\n",
    "  \"project_name\": project_name,\n",
    "  \"model_name\": model_name,\n",
    "  \"repo_id\": username+'/'+repo_name,\n",
    "  \"train_data\": train_data,\n",
    "  \"validation_data\": validation_data,\n",
    "  \"data_directory\": data_directory,\n",
    "  \"block_size\": block_size,\n",
    "  \"model_max_length\": max_token_length,\n",
    "  \"logging_steps\": -1,\n",
    "  \"evaluation_strategy\": \"epoch\",\n",
    "  \"save_total_limit\": 1,\n",
    "  \"save_strategy\": \"epoch\",\n",
    "  \"mixed_precision\": \"fp16\",\n",
    "  \"lr\": 0.00003,\n",
    "  \"epochs\": 3,\n",
    "  \"batch_size\": 2,\n",
    "  \"warmup_ratio\": 0.1,\n",
    "  \"gradient_accumulation\": 1,\n",
    "  \"optimizer\": \"adamw_torch\",\n",
    "  \"scheduler\": \"linear\",\n",
    "  \"weight_decay\": 0,\n",
    "  \"max_grad_norm\": 1,\n",
    "  \"seed\": 42,\n",
    "  \"quantization\": \"int4\",\n",
    "  \"lora_r\": 16,\n",
    "  \"lora_alpha\": 32,\n",
    "  \"lora_dropout\": 0.05\n",
    "}\n",
    "for key, value in model_params.items():\n",
    "  os.environ[key] = str(value)\n",
    "\n",
    "print(model_params)\n",
    "\n",
    "\n",
    "# Save parameters to environment variables\n",
    "# os.environ[\"project_name\"] = project_name\n",
    "# os.environ[\"model_name\"] = model_name\n",
    "# os.environ[\"repo_id\"] = username+'/'+repo_name\n",
    "# os.environ[\"train_data\"] = train_data   \n",
    "# os.environ[\"validation_data\"] = validation_data\n",
    "# os.environ[\"data_directory\"] = data_directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "⚠️ WARNING | 2023-12-22 10:41:00 | autotrain.cli.run_dreambooth:<module>:14 - ❌ Some DreamBooth components are missing! Please run `autotrain setup` to install it. Ignore this warning if you are not using DreamBooth or running `autotrain setup` already.\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/dsmueller/Repositories/HuggingFace/autotrain-playground/.venv/bin/autotrain\", line 8, in <module>\n",
      "    sys.exit(main())\n",
      "             ^^^^^^\n",
      "  File \"/home/dsmueller/Repositories/HuggingFace/autotrain-playground/.venv/lib/python3.11/site-packages/autotrain/cli/autotrain.py\", line 47, in main\n",
      "    command = args.func(args)\n",
      "              ^^^^^^^^^^^^^^^\n",
      "  File \"/home/dsmueller/Repositories/HuggingFace/autotrain-playground/.venv/lib/python3.11/site-packages/autotrain/cli/run_llm.py\", line 14, in run_llm_command_factory\n",
      "    return RunAutoTrainLLMCommand(args)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/home/dsmueller/Repositories/HuggingFace/autotrain-playground/.venv/lib/python3.11/site-packages/autotrain/cli/run_llm.py\", line 473, in __init__\n",
      "    raise ValueError(\"No GPU/MPS device found. LLM training requires an accelerator\")\n",
      "ValueError: No GPU/MPS device found. LLM training requires an accelerator\n"
     ]
    },
    {
     "ename": "CalledProcessError",
     "evalue": "Command '\nautotrain llm --train     --trainer sft     --project_name ./llms/ams_data_train-100_6abb23dc-cb9d-428e-9079-e47deee0edd9     --model mistralai/Mistral-7B-v0.1     --data_path ./fine_tune_data/     --train_split train_data     --valid_split validation_data     --repo_id ai-aerospace/ams-data-train-100-4601c8c8-0903-4f18-a6e8-1d2a40a697ce     --push_to_hub     --token HUGGINGFACE_TOKEN     --block_size 2242     --model_max_length 1121     --logging_steps -1     --evaluation_strategy epoch     --save_total_limit 1     --save_strategy epoch     --fp16     --lr 3e-05     --num_train_epochs 3     --train_batch_size 2     --warmup_ratio 0.1     --gradient_accumulation 1     --optimizer adamw_torch     --scheduler linear     --weight_decay 0     --max_grad_norm 1     --seed 42     --use_int4     --use-peft     --lora_r 16     --lora_alpha 32     --lora_dropout 0.05\n' returned non-zero exit status 1.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[50], line 40\u001b[0m\n\u001b[1;32m      4\u001b[0m command\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;124mautotrain llm --train \u001b[39m\u001b[38;5;130;01m\\\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;124m    --trainer sft \u001b[39m\u001b[38;5;130;01m\\\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;124m    --lora_dropout \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlora_dropout\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# Use subprocess.run() to execute the command\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcommand\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.11/subprocess.py:571\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    569\u001b[0m     retcode \u001b[38;5;241m=\u001b[39m process\u001b[38;5;241m.\u001b[39mpoll()\n\u001b[1;32m    570\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m check \u001b[38;5;129;01mand\u001b[39;00m retcode:\n\u001b[0;32m--> 571\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m CalledProcessError(retcode, process\u001b[38;5;241m.\u001b[39margs,\n\u001b[1;32m    572\u001b[0m                                  output\u001b[38;5;241m=\u001b[39mstdout, stderr\u001b[38;5;241m=\u001b[39mstderr)\n\u001b[1;32m    573\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m CompletedProcess(process\u001b[38;5;241m.\u001b[39margs, retcode, stdout, stderr)\n",
      "\u001b[0;31mCalledProcessError\u001b[0m: Command '\nautotrain llm --train     --trainer sft     --project_name ./llms/ams_data_train-100_6abb23dc-cb9d-428e-9079-e47deee0edd9     --model mistralai/Mistral-7B-v0.1     --data_path ./fine_tune_data/     --train_split train_data     --valid_split validation_data     --repo_id ai-aerospace/ams-data-train-100-4601c8c8-0903-4f18-a6e8-1d2a40a697ce     --push_to_hub     --token HUGGINGFACE_TOKEN     --block_size 2242     --model_max_length 1121     --logging_steps -1     --evaluation_strategy epoch     --save_total_limit 1     --save_strategy epoch     --fp16     --lr 3e-05     --num_train_epochs 3     --train_batch_size 2     --warmup_ratio 0.1     --gradient_accumulation 1     --optimizer adamw_torch     --scheduler linear     --weight_decay 0     --max_grad_norm 1     --seed 42     --use_int4     --use-peft     --lora_r 16     --lora_alpha 32     --lora_dropout 0.05\n' returned non-zero exit status 1."
     ]
    }
   ],
   "source": [
    "\n",
    "# Set .venv and execute the autotrain script\n",
    "# To see all parameters: autotrain llm --help\n",
    "# !autotrain llm --train --project_name my-llm --model TinyLlama/TinyLlama-1.1B-Chat-v0.1 --data_path . --use-peft --use_int4 --learning_rate 2e-4 --train_batch_size 6 --num_train_epochs 3 --trainer sft\n",
    "command=f\"\"\"\n",
    "autotrain llm --train \\\n",
    "    --trainer sft \\\n",
    "    --project_name {model_params['project_name']} \\\n",
    "    --model {model_params['model_name']} \\\n",
    "    --data_path {model_params['data_directory']} \\\n",
    "    --train_split {model_params['train_data']} \\\n",
    "    --valid_split {model_params['validation_data']} \\\n",
    "    --repo_id {model_params['repo_id']} \\\n",
    "    --push_to_hub \\\n",
    "    --token HUGGINGFACE_TOKEN \\\n",
    "    --block_size {model_params['block_size']} \\\n",
    "    --model_max_length {model_params['model_max_length']} \\\n",
    "    --logging_steps {model_params['logging_steps']} \\\n",
    "    --evaluation_strategy {model_params['evaluation_strategy']} \\\n",
    "    --save_total_limit {model_params['save_total_limit']} \\\n",
    "    --save_strategy {model_params['save_strategy']} \\\n",
    "    --fp16 \\\n",
    "    --lr {model_params['lr']} \\\n",
    "    --num_train_epochs {model_params['epochs']} \\\n",
    "    --train_batch_size {model_params['batch_size']} \\\n",
    "    --warmup_ratio {model_params['warmup_ratio']} \\\n",
    "    --gradient_accumulation {model_params['gradient_accumulation']} \\\n",
    "    --optimizer {model_params['optimizer']} \\\n",
    "    --scheduler linear \\\n",
    "    --weight_decay {model_params['weight_decay']} \\\n",
    "    --max_grad_norm {model_params['max_grad_norm']} \\\n",
    "    --seed {model_params['seed']} \\\n",
    "    --use_int4 \\\n",
    "    --use-peft \\\n",
    "    --lora_r {model_params['lora_r']} \\\n",
    "    --lora_alpha {model_params['lora_alpha']} \\\n",
    "    --lora_dropout {model_params['lora_dropout']}\n",
    "\"\"\"\n",
    "\n",
    "# Use subprocess.run() to execute the command\n",
    "subprocess.run(command, shell=True, check=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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