#!/usr/bin/env python3 """ Styling Training Pipeline using Unsloth and SFTTrainer Supports style transfer tasks with LoRA fine-tuning """ import os import sys import json import logging import argparse from pathlib import Path from typing import Dict, Any, Optional import yaml # Add the project root to the path sys.path.append(str(Path(__file__).parent.parent.parent)) from utils.config.config_manager import ConfigManager #from utils.logging.logging import setup_logging # Training imports import torch from datasets import load_from_disk, Dataset from unsloth import FastLanguageModel, is_bfloat16_supported from trl import SFTTrainer from transformers import TrainingArguments logger = logging.getLogger(__name__) class StylingTrainer: """Styling task trainer using Unsloth and SFTTrainer""" def __init__(self, config: Dict[str, Any]): self.config = config self.model = None self.tokenizer = None self.trainer = None # Set device self.device = "cuda" if torch.cuda.is_available() else "cpu" logger.info(f"Using device: {self.device}") # Training parameters self.max_seq_length = config.get('max_seq_length', 2048) self.dtype = config.get('dtype', None) self.load_in_4bit = config.get('load_in_4bit', True) self.hf_token = config.get('hf_token', None) # LoRA parameters self.lora_r = config.get('lora_r', 16) self.lora_alpha = config.get('lora_alpha', 16) self.lora_dropout = config.get('lora_dropout', 0) self.target_modules = config.get('target_modules', [ "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj" ]) # Training arguments self.batch_size = config.get('batch_size', 2) self.gradient_accumulation_steps = config.get('gradient_accumulation_steps', 4) self.learning_rate = config.get('learning_rate', 2e-4) self.num_epochs = config.get('num_epochs', 1) self.max_steps = config.get('max_steps', None) self.warmup_steps = config.get('warmup_steps', 5) self.weight_decay = config.get('weight_decay', 0.01) self.seed = config.get('seed', 3407) # Output paths self.output_dir = config.get('output_dir', './outputs') self.model_output_dir = config.get('model_output_dir', './models/styling') def load_model_and_tokenizer(self): """Load the pre-trained model and tokenizer""" logger.info("Loading model and tokenizer...") try: self.model, self.tokenizer = FastLanguageModel.from_pretrained( model_name=self.config['model_name'], max_seq_length=self.max_seq_length, dtype=self.dtype, load_in_4bit=self.load_in_4bit, token=self.hf_token ) logger.info(f"✅ Model loaded: {self.config['model_name']}") logger.info(f"✅ Tokenizer loaded with vocab size: {self.tokenizer.vocab_size}") except Exception as e: logger.error(f"❌ Error loading model: {e}") raise def setup_lora(self): """Setup LoRA for efficient fine-tuning""" logger.info("Setting up LoRA configuration...") try: self.model = FastLanguageModel.get_peft_model( self.model, r=self.lora_r, target_modules=self.target_modules, lora_alpha=self.lora_alpha, lora_dropout=self.lora_dropout, bias="none", use_gradient_checkpointing="unsloth", random_state=self.seed, use_rslora=False, loftq_config=None ) logger.info(f"✅ LoRA configured with r={self.lora_r}, alpha={self.lora_alpha}") except Exception as e: logger.error(f"❌ Error setting up LoRA: {e}") raise def load_dataset(self, dataset_path: str) -> Dataset: """Load the training dataset""" logger.info(f"Loading dataset from: {dataset_path}") try: if Path(dataset_path).exists(): # Check if it's a HuggingFace dataset directory if (Path(dataset_path) / "dataset_info.json").exists(): # Load from HuggingFace dataset directory dataset = load_from_disk(dataset_path) logger.info(f"Loaded HuggingFace dataset from disk: {len(dataset)} samples") else: # Load from processed data files (JSONL format) logger.info("Loading from processed data files...") from datasets import Dataset import json all_data = [] data_dir = Path(dataset_path) # Look for train.jsonl, validation.jsonl, test.jsonl for split_file in ["train.jsonl", "validation.jsonl", "test.jsonl"]: file_path = data_dir / split_file if file_path.exists(): logger.info(f"Loading {split_file}...") with open(file_path, 'r', encoding='utf-8') as f: for line in f: if line.strip(): data = json.loads(line) all_data.append(data) if not all_data: raise ValueError(f"No data found in {dataset_path}") # Create HuggingFace dataset dataset = Dataset.from_list(all_data) logger.info(f"Created HuggingFace dataset from {len(all_data)} samples") else: # Try loading from HuggingFace Hub logger.info(f"Attempting to load from HuggingFace Hub: {dataset_path}") dataset = Dataset.load_dataset(dataset_path, split="train") logger.info(f"Loaded from HuggingFace Hub: {len(dataset)} samples") logger.info(f"Dataset loaded: {len(dataset)} samples") logger.info(f"Dataset features: {dataset.features}") # Verify required fields exist required_fields = ["instruction", "input", "output"] missing_fields = [field for field in required_fields if field not in dataset.features] if missing_fields: raise ValueError(f"Missing required fields in dataset: {missing_fields}") return dataset except Exception as e: logger.error(f"Error loading dataset: {e}") raise def setup_trainer(self, train_dataset: Dataset): """Setup the SFTTrainer""" logger.info("Setting up SFTTrainer...") try: # First, map the dataset to create the text field with EOS token def formatting_prompts_func(examples): instructions = examples["instruction"] inputs = examples["input"] outputs = examples["output"] texts = [] for instruction, input_text, output in zip(instructions, inputs, outputs): # Must add EOS_TOKEN, otherwise your generation will go on forever! alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that follows the instruction ### Instruction: {} ### Input: {} ### Response: {}""" text = alpaca_prompt.format(instruction, input_text, output) + self.tokenizer.eos_token texts.append(text) return {"text": texts} # Apply the formatting function to create the text field logger.info("Mapping dataset to create text field with EOS token...") formatted_dataset = train_dataset.map(formatting_prompts_func, batched=True, remove_columns=train_dataset.column_names) logger.info(f"Dataset mapped successfully. New features: {formatted_dataset.features}") logger.info(f"Sample text field: {formatted_dataset[0]['text'][:100]}...") # Training arguments training_args = TrainingArguments( per_device_train_batch_size=self.batch_size, gradient_accumulation_steps=self.gradient_accumulation_steps, warmup_steps=self.warmup_steps, num_train_epochs=self.num_epochs, max_steps=self.max_steps, learning_rate=self.learning_rate, fp16=not is_bfloat16_supported(), bf16=is_bfloat16_supported(), logging_steps=1, optim="adamw_8bit", weight_decay=self.weight_decay, lr_scheduler_type="linear", seed=self.seed, output_dir=self.output_dir, report_to="none", # Disable wandb for now save_strategy="epoch", save_total_limit=2, evaluation_strategy="no", # No validation for now load_best_model_at_end=False, remove_unused_columns=False, dataloader_pin_memory=False, ) # Create trainer with the formatted dataset self.trainer = SFTTrainer( model=self.model, tokenizer=self.tokenizer, train_dataset=formatted_dataset, # Use the formatted dataset dataset_text_field="text", # The field we just created max_seq_length=self.max_seq_length, dataset_num_proc=2, packing=False, # Can make training 5x faster for short sequences args=training_args ) logger.info("SFTTrainer configured successfully") except Exception as e: logger.error(f"Error setting up trainer: {e}") raise def train(self, dataset_path: str): """Run the training process""" logger.info("🚀 Starting training process...") try: # Load model and tokenizer self.load_model_and_tokenizer() # Setup LoRA self.setup_lora() # Load dataset train_dataset = self.load_dataset(dataset_path) # Setup trainer self.setup_trainer(train_dataset) # Start training logger.info("Starting training...") trainer_stats = self.trainer.train() logger.info("✅ Training completed successfully!") logger.info(f"Training stats: {trainer_stats}") # Save the model self.save_model() return trainer_stats except Exception as e: logger.error(f"❌ Training failed: {e}") raise def save_model(self): """Save the trained model""" logger.info("Saving trained model...") try: # Create output directory Path(self.model_output_dir).mkdir(parents=True, exist_ok=True) # Save model and tokenizer self.model.save_pretrained(self.model_output_dir) self.tokenizer.save_pretrained(self.model_output_dir) # Save training config config_path = Path(self.model_output_dir) / "training_config.json" with open(config_path, 'w') as f: json.dump(self.config, f, indent=2) logger.info(f"✅ Model saved to: {self.model_output_dir}") except Exception as e: logger.error(f"❌ Error saving model: {e}") raise def prepare_for_inference(self): """Prepare model for inference""" logger.info("Preparing model for inference...") try: FastLanguageModel.for_inference(self.model) logger.info("✅ Model prepared for inference") except Exception as e: logger.error(f"❌ Error preparing for inference: {e}") raise def load_training_config(config_path: str) -> Dict[str, Any]: """Load training configuration from YAML file""" try: with open(config_path, 'r', encoding='utf-8') as f: config = yaml.safe_load(f) # Extract training configuration training_config = {} # Model configuration if 'model' in config: model_data = config['model'] training_config.update({ 'model_name': model_data.get('training_model', 'unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit'), 'max_seq_length': model_data.get('training_max_seq_length', 2048), 'dtype': model_data.get('training_dtype'), 'load_in_4bit': model_data.get('training_load_in_4bit', True), 'hf_token': model_data.get('training_token') }) # Training configuration if 'training' in config: training_data = config['training'] training_config.update({ 'num_epochs': training_data.get('num_epochs', 3), 'batch_size': training_data.get('batch_size', 2), 'learning_rate': training_data.get('learning_rate', 2e-4), 'weight_decay': training_data.get('weight_decay', 0.01), 'warmup_ratio': training_data.get('warmup_ratio', 0.1), 'lr_scheduler_type': training_data.get('lr_scheduler_type', 'linear') }) # Data configuration - use output_dir from data section if 'data' in config: data_config = config['data'] output_dir = data_config.get('output_dir', './data/processed/styling') training_config.update({ 'data_output_dir': output_dir, 'dataset_path': output_dir, # Default dataset path is the output_dir 'style_instruction': data_config.get('instruction', 'Rewrite the following text in a formal style') }) # LoRA configuration training_config.update({ 'lora_r': 16, 'lora_alpha': 16, 'lora_dropout': 0, 'target_modules': [ "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj" ], 'gradient_accumulation_steps': 4, 'max_steps': None, 'warmup_steps': 5, 'seed': 3407, 'output_dir': './outputs', 'model_output_dir': './models/styling' }) return training_config except Exception as e: logger.error(f"Error loading training config: {e}") raise def main(): """Main training function""" parser = argparse.ArgumentParser(description="Styling Training Pipeline") # Configuration parser.add_argument("--config", type=str, required=True, help="Path to YAML configuration file") parser.add_argument("--dataset", type=str, help="Path to training dataset (HF dataset path or local path)") parser.add_argument("--output-dir", type=str, help="Output directory for model") parser.add_argument("--epochs", type=int, help="Number of training epochs") parser.add_argument("--batch-size", type=int, help="Training batch size") parser.add_argument("--learning-rate", type=float, help="Learning rate") parser.add_argument("--max-steps", type=int, help="Maximum training steps") args = parser.parse_args() # Setup logging # setup_logging() # Commented out as per user's change try: # Load configuration logger.info(f"Loading configuration from: {args.config}") training_config = load_training_config(args.config) # Override with CLI arguments if args.output_dir: training_config['model_output_dir'] = args.output_dir if args.epochs: training_config['num_epochs'] = args.epochs if args.batch_size: training_config['batch_size'] = args.batch_size if args.learning_rate: training_config['learning_rate'] = args.learning_rate if args.max_steps: training_config['max_steps'] = args.max_steps # Determine dataset path: CLI argument takes precedence, then YAML config dataset_path = args.dataset or training_config.get('dataset_path') if not dataset_path: logger.error("No dataset path provided. Use --dataset or ensure output_dir is set in YAML config.") sys.exit(1) logger.info("Training configuration:") for key, value in training_config.items(): logger.info(f" {key}: {value}") logger.info(f" Dataset path: {dataset_path}") # Initialize trainer trainer = StylingTrainer(training_config) # Start training trainer.train(dataset_path) logger.info("Training completed successfully!") except Exception as e: logger.error(f"Training failed: {e}") sys.exit(1) if __name__ == "__main__": main()