#!/usr/bin/env python3 """ Train LoRA adapter on Cyron summary dataset. Uses Hugging Face TRL for SFT training. """ import argparse import os import yaml from pathlib import Path import torch def train(config_path): """Train LoRA adapter using TRL.""" with open(config_path) as f: config = yaml.safe_load(f) from transformers import ( AutoModelForCausalLM, AutoTokenizer, TrainingArguments, ) from peft import ( LoraConfig, get_peft_model, ) from trl import SFTTrainer print(f"Loading model: {config['base_model']}") # Load model and convert to bf16 (remove NVFP4 quantization) print(f"Loading model: {config['base_model']}") model = AutoModelForCausalLM.from_pretrained( config["base_model"], torch_dtype=torch.bfloat16, device_map="cpu", # Load to CPU first trust_remote_code=True, # Override any quantization config (NVFP4 -> bf16) _fast_init=False, ) print("Model loaded and converted to bf16.") # Add LoRA lora_config = LoraConfig( r=config["lora_r"], lora_alpha=config["lora_alpha"], lora_dropout=config["lora_dropout"], target_modules=config["target_modules"], task_type=config.get("lora_task_type", "CAUSAL_LM"), ) model = get_peft_model(model, lora_config) model.print_trainable_parameters() # Load tokenizer tokenizer = AutoTokenizer.from_pretrained(config["base_model"]) tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "right" # Load dataset from datasets import load_dataset import os # Get dataset paths - dataset is in training/data/ relative to repo root repo_root = Path(__file__).parent.parent.parent train_path = str(repo_root / "training" / "data" / "train.jsonl") test_path = str(repo_root / "training" / "data" / "test.jsonl") dataset = load_dataset( "json", data_files={ "train": train_path, "test": test_path, }, ) # Training arguments (max_seq_length removed - passed to SFTTrainer instead) training_args = TrainingArguments( output_dir=config["train_params"]["output_dir"], num_train_epochs=config["train_params"]["num_train_epochs"], per_device_train_batch_size=config["train_params"]["per_device_train_batch_size"], gradient_accumulation_steps=config["train_params"]["gradient_accumulation_steps"], learning_rate=config["train_params"]["learning_rate"], lr_scheduler_type=config["train_params"]["lr_scheduler_type"], weight_decay=config["train_params"]["weight_decay"], warmup_ratio=config["train_params"]["warmup_ratio"], logging_steps=config["train_params"]["logging_steps"], save_steps=config["train_params"]["save_steps"], save_total_limit=config["train_params"]["save_total_limit"], eval_strategy=config.get("eval_strategy", "steps"), eval_steps=config.get("eval_steps", 100), bf16=config.get("mixed_precision", "bf16") == "bf16", fp16=config.get("mixed_precision", "bf16") == "fp16", gradient_checkpointing=config.get("gradient_checkpointing", True), ) # SFT Trainer (DeepSpeed handles distributed training via config) from trl import SFTTrainer trainer = SFTTrainer( model=model, processing_class=tokenizer, train_dataset=dataset["train"], eval_dataset=dataset["test"], args=training_args, ) # Train print("Starting training...") trainer.train() # Save trainer.save_model(config["train_params"]["output_dir"]) tokenizer.save_pretrained(config["train_params"]["output_dir"]) print(f"Training complete! Model saved to {config['train_params']['output_dir']}") def main(): parser = argparse.ArgumentParser(description="Train LoRA adapter") parser.add_argument("--config", type=str, default="configs/llama2-7b-lora.yaml", help="Training configuration file") parser.add_argument("--check-only", action="store_true", help="Validate config and dependencies without training") args = parser.parse_args() if args.check_only: check_setup(args.config) else: train(args.config) def check_setup(config_path): """Validate config and dependencies without loading model.""" print("=== Checking Setup ===") # Check config file print(f"\n1. Config file: {config_path}") if not Path(config_path).exists(): print(f" ERROR: Config file not found: {config_path}") return False print(" ✓ Config file exists") # Load and validate config with open(config_path) as f: config = yaml.safe_load(f) print(" ✓ Config file is valid YAML") # Check model path print(f"\n2. Model path: {config['base_model']}") if Path(config['base_model']).exists(): print(f" ✓ Model path exists: {config['base_model']}") else: print(f" ⚠ Model path not found: {config['base_model']}") print(" (Will download from HuggingFace during training)") # Check dataset files print("\n3. Dataset files:") repo_root = Path(__file__).parent.parent.parent train_path = repo_root / "training" / "data" / "train.jsonl" test_path = repo_root / "training" / "data" / "test.jsonl" if train_path.exists(): print(f" ✓ Train data: {train_path}") else: print(f" ✗ Train data missing: {train_path}") if test_path.exists(): print(f" ✓ Test data: {test_path}") else: print(f" ✗ Test data missing: {test_path}") # Check GPU print("\n4. GPU:") import torch if torch.cuda.is_available(): print(f" ✓ GPU available: {torch.cuda.get_device_name(0)}") print(f" ✓ VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB") print(f" ✓ GPU count: {torch.cuda.device_count()}") else: print(" ✗ No GPU detected!") return False # Check required packages print("\n5. Required packages:") packages = ['transformers', 'datasets', 'trl', 'peft', 'accelerate', 'deepspeed'] for pkg in packages: try: __import__(pkg) print(f" ✓ {pkg}") except ImportError: print(f" ✗ {pkg} not installed") # Check DeepSpeed config print("\n6. DeepSpeed config:") if 'deepspeed_config' in config: print(" ✓ DeepSpeed config present") ds_config = config['deepspeed_config'] if 'zero_optimization' in ds_config: stage = ds_config['zero_optimization'].get('stage', 'N/A') print(f" ✓ ZeRO stage: {stage}") else: print(" ✗ No DeepSpeed config found") print("\n=== Check Complete ===") print("If all checks pass, you can run training with:") print(f" bash train-on-this-server.sh") if __name__ == "__main__": main()