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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"from trl import SFTTrainer\n",
"from peft import LoraConfig, get_peft_model\n",
"\n",
"import os\n",
"from uuid import uuid4\n",
"import pandas as pd\n",
"\n",
"import subprocess\n",
"import evaluate\n",
"import transformers\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"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": 9,
"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)\n",
"\n",
"# dataset = load_dataset(\"imdb\", split=\"train\")\n",
"dataset_name = 'ai-aerospace/ams_data_train_generic_v0.1_100'\n",
"dataset = load_dataset(dataset_name)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Max token length train: 1121\n",
"Max token length validation: 38\n",
"Block size: 2242\n",
"{'project_name': './llms/ams_data_train-100_4ba55532-e0b2-478b-9f5b-beb082e1b557', 'model_name': 'mistralai/Mistral-7B-v0.1', 'repo_id': 'ai-aerospace/ams-data-train-100-11b94ea4-2b2b-4db3-9e69-acb5a5d9f3e8', 'train_data': 'train_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": [
"# 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",
"# max_token_length=max_token_length_train\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())\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"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"### Start trainer\n",
"# trainer = SFTTrainer(\n",
"# model_name,\n",
"# train_dataset=dataset,\n",
"# dataset_text_field=\"text\",\n",
"# max_seq_length=512,\n",
"# )\n",
"\n",
"peft_config = LoraConfig(\n",
" r=model_params['lora_r'],\n",
" lora_alpha=model_params['lora_alpha'],\n",
" lora_dropout=model_params['lora_dropout']\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the model\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" load_in_4bit=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Setting up the LoRA model\n",
"# import os\n",
"# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
"# from transformers import AutoModelForSequenceClassification\n",
"# from peft import LoraConfig, get_peft_model, TaskType\n",
"\n",
"# MODEL =\"xlm-roberta-large\"\n",
"\n",
"# config = LoraConfig(\n",
"# task_type=\"SEQ_CLS\",\n",
"# r=16,\n",
"# lora_alpha=16,\n",
"# target_modules=[\"query\", \"value\"], # Targets the attention blocks in the model\n",
"# lora_dropout=0.1,\n",
"# bias=\"none\",\n",
"# modules_to_save=[\"classifier\"],\n",
"# )\n",
"\n",
"# model = AutoModelForSequenceClassification.from_pretrained(\n",
"# MODEL,\n",
"# num_labels=len(unique_subissues),\n",
"# id2label=id2label,\n",
"# label2id=label2id,\n",
"# ignore_mismatched_sizes=True\n",
"# ) \n",
"\n",
"lora_model = get_peft_model(model, peft_config)\n",
"lora_model.print_trainable_parameters()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"trainer = SFTTrainer(\n",
" model,\n",
" train_dataset=dataset,\n",
" dataset_text_field=\"text\",\n",
" peft_config=peft_config,\n",
" max_seq_length=model_params['model_max_length']\n",
")\n",
"\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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{
"ename": "NameError",
"evalue": "name 'lora_model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[8], line 18\u001b[0m\n\u001b[1;32m 13\u001b[0m results\u001b[38;5;241m.\u001b[39mupdate(precision_metric\u001b[38;5;241m.\u001b[39mcompute(predictions\u001b[38;5;241m=\u001b[39mpredictions, references \u001b[38;5;241m=\u001b[39m labels, average\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmacro\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[1;32m 17\u001b[0m trainer \u001b[38;5;241m=\u001b[39m transformers\u001b[38;5;241m.\u001b[39mTrainer(\n\u001b[0;32m---> 18\u001b[0m model\u001b[38;5;241m=\u001b[39m\u001b[43mlora_model\u001b[49m,\n\u001b[1;32m 19\u001b[0m train_dataset\u001b[38;5;241m=\u001b[39mtrain_dataset,\n\u001b[1;32m 20\u001b[0m eval_dataset\u001b[38;5;241m=\u001b[39mval_dataset,\n\u001b[1;32m 21\u001b[0m compute_metrics\u001b[38;5;241m=\u001b[39mcompute_metrics,\n\u001b[1;32m 22\u001b[0m args\u001b[38;5;241m=\u001b[39mtransformers\u001b[38;5;241m.\u001b[39mTrainingArguments(\n\u001b[1;32m 23\u001b[0m per_device_train_batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8\u001b[39m,\n\u001b[1;32m 24\u001b[0m per_device_eval_batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m,\n\u001b[1;32m 25\u001b[0m gradient_accumulation_steps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m,\n\u001b[1;32m 26\u001b[0m warmup_steps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m,\n\u001b[1;32m 27\u001b[0m max_steps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m12276\u001b[39m,\n\u001b[1;32m 28\u001b[0m learning_rate\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2e-4\u001b[39m,\n\u001b[1;32m 29\u001b[0m fp16\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 30\u001b[0m eval_steps\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1000\u001b[39m,\n\u001b[1;32m 31\u001b[0m logging_steps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m,\n\u001b[1;32m 32\u001b[0m save_steps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m,\n\u001b[1;32m 33\u001b[0m evaluation_strategy\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msteps\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 34\u001b[0m do_eval\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 35\u001b[0m load_best_model_at_end\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 36\u001b[0m metric_for_best_model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mf1\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 37\u001b[0m output_dir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_outputs\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 38\u001b[0m logging_dir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_outputs\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 39\u001b[0m remove_unused_columns \u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \n\u001b[1;32m 40\u001b[0m report_to\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwandb\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;66;03m# enable logging to W&B\u001b[39;00m\n\u001b[1;32m 41\u001b[0m ),\n\u001b[1;32m 42\u001b[0m )\n\u001b[1;32m 43\u001b[0m trainer\u001b[38;5;241m.\u001b[39mtrain()\n",
"\u001b[0;31mNameError\u001b[0m: name 'lora_model' is not defined"
]
}
],
"source": [
"f1_metric = evaluate.load(\"f1\")\n",
"recall_metric = evaluate.load(\"recall\")\n",
"accuracy_metric = evaluate.load(\"accuracy\")\n",
"precision_metric = evaluate.load(\"precision\")\n",
"\n",
"def compute_metrics(eval_pred):\n",
" logits, labels = eval_pred\n",
" predictions = np.argmax(logits, axis=-1)\n",
" results = {}\n",
" results.update(f1_metric.compute(predictions=predictions, references = labels, average=\"macro\"))\n",
" results.update(recall_metric.compute(predictions=predictions, references = labels, average=\"macro\"))\n",
" results.update(accuracy_metric.compute(predictions=predictions, references = labels))\n",
" results.update(precision_metric.compute(predictions=predictions, references = labels, average=\"macro\"))\n",
"\n",
" return results\n",
"\n",
"# See https://towardsdatascience.com/fine-tune-your-llm-without-maxing-out-your-gpu-db2278603d78 for details\n",
"trainer = transformers.Trainer(\n",
" model=lora_model,\n",
" train_dataset=model_params['train_data'],\n",
" eval_dataset=model_params['validation_data'],\n",
" compute_metrics=compute_metrics,\n",
" args=transformers.TrainingArguments(\n",
" per_device_train_batch_size=model_params['batch_size'],\n",
" per_device_eval_batch_size=model_params['batch_size'],\n",
" gradient_accumulation_steps=model_params['gradient_accumulation'],\n",
" warmup_steps=100,\n",
" max_steps=12276,\n",
" learning_rate=model_params['lr'],\n",
" fp16=True,\n",
" eval_steps= 1000,\n",
" logging_steps=1000,\n",
" save_steps=1000,\n",
" evaluation_strategy=model_params['evaluation_strategy'],\n",
" do_eval=True,\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"f1\",\n",
" output_dir='model_outputs',\n",
" logging_dir='model_outputs',\n",
" remove_unused_columns =False, \n",
" report_to='wandb' # enable logging to W&B\n",
" ),\n",
")\n",
"trainer.train()"
]
}
],
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