run_transformers_training.py

#!/usr/bin/env python
# coding=utf-8

import os
import sys
import json
import argparse
import logging
from datetime import datetime
import time

# Import Unsloth first, before other ML imports
try:
    from unsloth import FastLanguageModel
    from unsloth.chat_templates import get_chat_template
    unsloth_available = True
except ImportError:
    unsloth_available = False
    logger = logging.getLogger(__name__)
    logger.warning("Unsloth not available. Please install with: pip install unsloth")

import torch
from datasets import load_dataset
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TrainingArguments,
    Trainer,
    TrainerCallback,
    set_seed,
    BitsAndBytesConfig
)

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s",
    handlers=[logging.StreamHandler(sys.stdout)]
)
logger = logging.getLogger(__name__)

# Set other loggers to WARNING to reduce noise and ensure our logs are visible
logging.getLogger("transformers").setLevel(logging.WARNING)
logging.getLogger("datasets").setLevel(logging.WARNING)
logging.getLogger("accelerate").setLevel(logging.WARNING)
logging.getLogger("torch").setLevel(logging.WARNING)
logging.getLogger("bitsandbytes").setLevel(logging.WARNING)

# Define a clean logging function for HF Space compatibility
def log_info(message):
    """Log information in a format compatible with Hugging Face Spaces"""
    # Just use the logger, but ensure consistent formatting
    logger.info(message)
    # Also ensure output is flushed immediately for streaming
    sys.stdout.flush()

# Check for BitsAndBytes
try:
    from transformers import BitsAndBytesConfig
    bitsandbytes_available = True
except ImportError:
    bitsandbytes_available = False
    logger.warning("BitsAndBytes not available. 4-bit quantization will not be used.")

# Check for PEFT
try:
    from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
    peft_available = True
except ImportError:
    peft_available = False
    logger.warning("PEFT not available. Parameter-efficient fine-tuning will not be used.")

def load_env_variables():
    """Load environment variables from system, .env file, or Hugging Face Space variables."""
    # Check if we're running in a Hugging Face Space
    if os.environ.get("SPACE_ID"):
        logging.info("Running in Hugging Face Space")
        
        # Log the presence of variables (without revealing values)
        logging.info(f"HF_TOKEN available: {bool(os.environ.get('HF_TOKEN'))}")
        logging.info(f"HF_USERNAME available: {bool(os.environ.get('HF_USERNAME'))}")
        
        # If username is not set, try to extract from SPACE_ID
        if not os.environ.get("HF_USERNAME") and "/" in os.environ.get("SPACE_ID", ""):
            username = os.environ.get("SPACE_ID").split("/")[0]
            os.environ["HF_USERNAME"] = username
            logging.info(f"Set HF_USERNAME from SPACE_ID: {username}")
    else:
        # Try to load from .env file if not in a Space
        try:
            from dotenv import load_dotenv
            # Updated path to .env file in the new directory structure
            env_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "shared", ".env")
            if os.path.exists(env_path):
                load_dotenv(env_path)
                logging.info(f"Loaded environment variables from {env_path}")
                logging.info(f"HF_TOKEN loaded from .env file: {bool(os.environ.get('HF_TOKEN'))}")
                logging.info(f"HF_USERNAME loaded from .env file: {bool(os.environ.get('HF_USERNAME'))}")
                logging.info(f"HF_SPACE_NAME loaded from .env file: {bool(os.environ.get('HF_SPACE_NAME'))}")
            else:
                logging.warning(f"No .env file found at {env_path}")
        except ImportError:
            logging.warning("python-dotenv not installed, not loading from .env file")
    
    if not os.environ.get("HF_USERNAME"):
        logger.warning("HF_USERNAME is not set. Using default username.")
    
    if not os.environ.get("HF_SPACE_NAME"):
        logger.warning("HF_SPACE_NAME is not set. Using default space name.")
        
    # Set HF_TOKEN for huggingface_hub
    if os.environ.get("HF_TOKEN"):
        os.environ["HUGGING_FACE_HUB_TOKEN"] = os.environ.get("HF_TOKEN")

def load_configs(base_path):
    """Load all configuration from a single consolidated file."""
    configs = {}
    
    # Using a single consolidated config file
    config_file = "transformers_config.json"
    
    file_path = os.path.join(base_path, config_file)
    try:
        with open(file_path, "r") as f:
            config = json.load(f)
            # Extract sections into separate config dictionaries for compatibility
            configs["transformers"] = config
            configs["hardware"] = config.get("hardware", {})
            configs["dataset"] = config.get("dataset", {})
            logger.info(f"Loaded consolidated configuration from {file_path}")
    except Exception as e:
        logger.error(f"Error loading {config_file}: {e}")
        raise
    
    return configs

def parse_args():
    parser = argparse.ArgumentParser(description="Fine-tune a language model on a text dataset")
    parser.add_argument("--config_dir", type=str, default=".", help="Directory containing configuration files")
    return parser.parse_args()

def load_model_and_tokenizer(config):
    """Load model and tokenizer with proper error handling and optimizations."""
    try:
        if not unsloth_available:
            logger.error("Unsloth is required for training with pre-quantized model")
            logger.error("Please ensure unsloth is in requirements.txt")
            raise ImportError("Unsloth is required for this training setup")
        
        # Get model name correctly from nested config structure
        model_name = config.get("model", {}).get("name") or config.get("model_name_or_path") or config.get("model_name")
        logger.info(f"Loading model: {model_name}")
        
        if not model_name:
            raise ValueError("Model name not found in configuration. Please check your transformers_config.json file.")
            
        logger.info("Using Unsloth optimizations with pre-quantized model")
        
        # Check for flash attention without importing it directly
        use_flash_attention = config.get("use_flash_attention", True)
        try:
            import flash_attn
            logger.info("Flash attention detected and will be used")
        except ImportError:
            use_flash_attention = False
            logger.warning("Flash attention not available, falling back to standard attention")
            
        # First detect if we have a GPU
        if torch.cuda.is_available():
            gpu_count = torch.cuda.device_count()
            logger.info(f"CUDA available, found {gpu_count} GPU(s)")
            
            # Log GPU info
            for i in range(gpu_count):
                logger.info(f"GPU {i}: {torch.cuda.get_device_name(i)}")
                logger.info(f"Memory: {torch.cuda.get_device_properties(i).total_memory / 1024**3:.2f} GB")
            
            # Create an optimized device map for better balance
            if gpu_count > 1:
                logger.info(f"Creating balanced device map for {gpu_count} GPUs")
                # Use auto mapping but with memory tracking
                device_map = "auto"
                # Set max memory for better balancing
                max_memory = {i: f"{int(torch.cuda.get_device_properties(i).total_memory * 0.85 / 1024**3)}GiB" for i in range(gpu_count)}
                logger.info(f"Max memory settings: {max_memory}")
            else:
                device_map = "auto"
                max_memory = None
        else:
            logger.warning("No CUDA available, falling back to CPU")
            device_map = {"": "cpu"}  # Force CPU placement
            max_memory = None
        
        # Set default dtype for better numerics
        if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8:
            # Use bfloat16 for Ampere or newer
            dtype = torch.bfloat16
            logger.info("Using bfloat16 precision (Ampere+ GPU)")
        elif torch.cuda.is_available():
            # Use float16 for older GPUs
            dtype = torch.float16
            logger.info("Using float16 precision (pre-Ampere GPU)")
        else:
            # CPU, use default dtype
            dtype = None
            logger.info("Using default precision (CPU)")
        
        # Load model with proper error handling for out-of-memory
        try:
            # Improved memory settings for multi-GPU setup
            os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
            
            model, tokenizer = FastLanguageModel.from_pretrained(
                model_name=model_name,
                max_seq_length=config.get("max_seq_length", 2048) or config.get("tokenizer", {}).get("max_seq_length", 2048),
                dtype=dtype,
                device_map=device_map,
                max_memory=max_memory,
                # Don't explicitly use flash attention config here, let Unsloth handle it
            )
        except RuntimeError as e:
            if "CUDA out of memory" in str(e):
                logger.error("Out of GPU memory. Consider using a smaller batch size or gradient accumulation steps.")
                raise
            else:
                # Try again with CPU placement to see if it's a memory issue
                logger.warning(f"Error loading model on default device: {str(e)}")
                logger.warning("Attempting to load with device_map='cpu' and no specific dtype")
                model, tokenizer = FastLanguageModel.from_pretrained(
                    model_name=model_name,
                    max_seq_length=config.get("max_seq_length", 2048) or config.get("tokenizer", {}).get("max_seq_length", 2048),
                    dtype=None,
                    device_map={"": "cpu"},
                )
                logger.warning("Model loaded on CPU. Training will be very slow.")
        
        # Ensure model and optimizer init is on the same device
        logger.info(f"Model device map: {model.hf_device_map if hasattr(model, 'hf_device_map') else 'Not available'}")
            
        # Apply Unsloth's training optimizations with config parameters
        unsloth_config = config.get("unsloth", {})
        model = FastLanguageModel.get_peft_model(
            model,
            r=unsloth_config.get("r", 32),
            target_modules=unsloth_config.get("target_modules", 
                ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]),
            lora_alpha=unsloth_config.get("alpha", 16),
            lora_dropout=unsloth_config.get("dropout", 0.05),
            bias="none",
            use_gradient_checkpointing=config.get("gradient_checkpointing", True) or config.get("training", {}).get("gradient_checkpointing", True),
            random_state=config.get("seed", 42),
        )
        logger.info("Unsloth optimizations applied successfully")

        # Set up tokenizer settings
        chat_template = config.get("chat_template") or config.get("tokenizer", {}).get("chat_template")
        if chat_template:
            try:
                template = get_chat_template("phi")
                tokenizer.chat_template = template
                logger.info("Set phi chat template")
            except Exception as e:
                logger.warning(f"Failed to set chat template: {str(e)}")
        
        # Ensure proper token settings
        if tokenizer.pad_token_id is None:
            tokenizer.pad_token_id = tokenizer.eos_token_id
            logger.info(f"Set pad_token_id to eos_token_id: {tokenizer.pad_token_id}")
        
        return model, tokenizer
    
    except Exception as e:
        logger.error(f"Error in model/tokenizer loading: {str(e)}")
        logger.error("If missing dependencies, check the requirements.txt file")
        raise

def load_dataset_with_mapping(dataset_config):
    """Load and prepare dataset with proper column mapping."""
    try:
        # Load dataset
        dataset_name = dataset_config.get("dataset", {}).get("name", "")
        dataset_split = dataset_config.get("dataset", {}).get("split", "train")
        
        if not dataset_name:
            raise ValueError("Dataset name not provided in configuration")
        
        logger.info(f"Loading dataset {dataset_name}, split {dataset_split}")
        dataset = load_dataset(dataset_name, split=dataset_split)
        
        # Map columns if specified - with checks to avoid conflicts
        column_mapping = dataset_config.get("dataset", {}).get("column_mapping", {})
        if column_mapping:
            logger.info(f"Checking column mapping: {column_mapping}")
            
            # Only apply mappings for columns that need renaming and don't already exist
            safe_mappings = {}
            for target, source in column_mapping.items():
                if source in dataset.column_names:
                    # Skip if target already exists and is not the same as source
                    if target in dataset.column_names and target != source:
                        logger.warning(f"Cannot rename '{source}' to '{target}' - target column already exists")
                    else:
                        safe_mappings[source] = target
            
            # Apply safe renames
            if safe_mappings:
                logger.info(f"Applying safe column mapping: {safe_mappings}")
                for source, target in safe_mappings.items():
                    if source != target:  # Only rename if names are different
                        dataset = dataset.rename_column(source, target)
        
        # Verify expected columns exist
        expected_columns = {"id", "conversations"}
        for col in expected_columns:
            if col not in dataset.column_names:
                # If "conversations" is missing but "text" exists, it might need conversion
                if col == "conversations" and "text" in dataset.column_names:
                    logger.info("Converting 'text' field to 'conversations' format")
                    
                    def convert_text_to_conversations(example):
                        # Check if text is already a list of conversation turns
                        if isinstance(example.get("text"), list):
                            return {"conversations": example["text"]}
                        # Otherwise, create a simple conversation with the text as user message
                        else:
                            return {
                                "conversations": [
                                    {"role": "user", "content": str(example.get("text", ""))}
                                ]
                            }
                    
                    dataset = dataset.map(convert_text_to_conversations)
                else:
                    logger.warning(f"Expected column '{col}' not found in dataset")
        
        # Note: Explicitly NOT sorting the dataset to preserve original order
        logger.info("Preserving original dataset order (no sorting)")
        
        # Log examples without printing full content
        if "conversations" in dataset.column_names:
            sample_ids = [example['id'] for example in dataset.select(range(min(5, len(dataset))))]
            logger.info(f"First few IDs: {sample_ids}")
            
            # Log conversation structure without full content
            if len(dataset) > 0:
                sample_conv_structure = []
                for msg in dataset["conversations"][0]:
                    if isinstance(msg, dict):
                        content = msg.get('content', '')
                        preview = content[:50] + "..." if len(content) > 50 else content
                        sample_conv_structure.append({
                            "role": msg.get('role', ''),
                            "content_length": len(content),
                            "preview": preview
                        })
                logger.info(f"Conversation structure: {sample_conv_structure}")
        
        logger.info(f"Dataset loaded successfully with {len(dataset)} examples")
        logger.info(f"Dataset columns: {dataset.column_names}")
        return dataset
        
    except Exception as e:
        logger.error(f"Error loading dataset: {str(e)}")
        raise

def format_phi_chat(messages, dataset_config):
    """Format messages according to phi-4's chat template and dataset config."""
    formatted_chat = ""
    
    # Get role templates from config
    roles = dataset_config.get("data_formatting", {}).get("roles", {
        "system": "System: {content}\n\n",
        "human": "Human: {content}\n\n",
        "user": "Human: {content}\n\n",
        "assistant": "Assistant: {content}\n\n"
    })
    
    # Handle research introduction metadata first
    metadata = next((msg for msg in messages if isinstance(msg, dict) and 
                    "[RESEARCH INTRODUCTION]" in msg.get("content", "")), None)
    if metadata:
        system_template = roles.get("system", "System: {content}\n\n")
        formatted_chat = system_template.format(content=metadata['content'])
        messages = [msg for msg in messages if msg != metadata]
    
    # Process remaining messages
    for message in messages:
        if not isinstance(message, dict) or "content" not in message:
            logger.warning(f"Skipping invalid message format: {message}")
            continue
            
        role = message.get("role", "").lower()
        content = message.get("content", "")
    
        # Format based on role
        if role == "human" or role == "user":
            template = roles.get("user", roles.get("human", "Human: {content}\n\n"))
            formatted_chat += template.format(content=content)
        elif role == "assistant" or role == "bot":
            template = roles.get("assistant", "Assistant: {content}\n\n")
            formatted_chat += template.format(content=content)
        elif role == "system":
            # For system messages, prepend them
            template = roles.get("system", "System: {content}\n\n")
            formatted_chat = template.format(content=content) + formatted_chat
        else:
            # Default to system for unknown roles
            logger.warning(f"Unknown role '{role}' - treating as system message")
            template = roles.get("system", "System: {content}\n\n")
            formatted_chat += template.format(content=content)
    
    return formatted_chat.strip()

class SimpleDataCollator:
    def __init__(self, tokenizer, dataset_config):
        self.tokenizer = tokenizer
        self.dataset_config = dataset_config
        self.stats = {"processed": 0, "skipped": 0, "total_tokens": 0}
        self.pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
        self.max_seq_length = dataset_config.get("dataset", {}).get("processing", {}).get("max_seq_length", 2048)
        logger.info(f"SimpleDataCollator initialized - using pre-audited dataset with max_seq_length={self.max_seq_length}")
        logger.info("Using exact dataset structure without reformatting")
        
        # Check if we're on GPU
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        logger.info(f"SimpleDataCollator using device: {self.device}")
    
    def __call__(self, features):
        """Process examples preserving exact JSONL structure"""
        batch = {"input_ids": [], "attention_mask": [], "labels": []}
        
        for example in features:
            try:
                # Get ID
                paper_id = example.get("id", "")
                
                # Get conversations - these should already contain role and content
                conversations = example.get("conversations", [])
                if not conversations:
                    self.stats["skipped"] += 1
                    continue
                
                # Directly use the conversations array as input to the model's chat template
                # This preserves the exact structure with roles and content as they are
                try:
                    # Let tokenizer handle the content with the model's chat template
                    inputs = self.tokenizer.apply_chat_template(
                        conversations,
                        return_tensors=None,
                        add_generation_prompt=False
                    )
                except Exception as chat_error:
                    # Fallback if apply_chat_template fails
                    logger.warning(f"Chat template application failed for example {paper_id}: {str(chat_error)[:100]}")
                    
                    # Create a basic representation of the conversation
                    conversation_text = ""
                    for msg in conversations:
                        if isinstance(msg, dict) and 'content' in msg:
                            conversation_text += msg.get('content', '') + "\n\n"
                    
                    # Basic tokenization
                    inputs = self.tokenizer(
                        conversation_text,
                        add_special_tokens=True,
                        return_tensors=None
                    )
                
                # Apply length cap if needed (shouldn't be necessary for pre-audited data)
                if self.max_seq_length > 0 and len(inputs) > self.max_seq_length:
                    logger.warning(f"Example {paper_id} exceeds max_seq_length ({len(inputs)} > {self.max_seq_length})")
                    inputs = inputs[:self.max_seq_length]
                    
                # Create attention mask (1 for all tokens)
                attention_mask = [1] * len(inputs)
                
                if len(inputs) > 0:
                    # For causal language modeling, labels are the same as inputs
                    labels = inputs.copy()
                    
                    batch["input_ids"].append(inputs)
                    batch["attention_mask"].append(attention_mask)
                    batch["labels"].append(labels)
                    
                    self.stats["processed"] += 1
                    self.stats["total_tokens"] += len(inputs)
                    
                    # Debug logging for first few examples
                    log_samples = self.dataset_config.get("validation", {}).get("log_samples", 3)
                    if self.stats["processed"] <= log_samples:
                        logger.info(f"Example {self.stats['processed']}:")
                        logger.info(f"Paper ID: {paper_id}")
                        logger.info(f"Token count: {len(inputs)}")
                        logger.info(f"Conversation entries: {len(conversations)}")
                else:
                    self.stats["skipped"] += 1
            except Exception as e:
                logger.warning(f"Error processing example: {str(e)[:100]}...")
                logger.warning(f"Problematic example ID: {example.get('id', 'unknown')}")
                self.stats["skipped"] += 1
                continue
        
        if not batch["input_ids"]:
            logger.warning("Empty batch, returning dummy tensors")
            return {
                "input_ids": torch.zeros((1, 1), dtype=torch.long),
                "attention_mask": torch.zeros((1, 1), dtype=torch.long),
                "labels": torch.zeros((1, 1), dtype=torch.long)
            }
        
        # Pad the batch
        max_length = max(len(ids) for ids in batch["input_ids"])
        
        for i in range(len(batch["input_ids"])):
            padding_length = max_length - len(batch["input_ids"][i])
            if padding_length > 0:
                batch["input_ids"][i].extend([self.pad_token_id] * padding_length)
                batch["attention_mask"][i].extend([0] * padding_length)
                batch["labels"][i].extend([-100] * padding_length)
        
        # Convert to tensors
        batch = {k: torch.tensor(v, dtype=torch.long) for k, v in batch.items()}
        
        # Log stats periodically
        log_interval = self.dataset_config.get("validation", {}).get("log_interval", 100)
        if self.stats["processed"] % log_interval == 0 and self.stats["processed"] > 0:
            logger.info(f"Data collator stats: processed={self.stats['processed']}, "
                       f"skipped={self.stats['skipped']}, "
                       f"avg_tokens={self.stats['total_tokens']/self.stats['processed']:.1f}")
        
        return batch

class LoggingCallback(TrainerCallback):
    def __init__(self):
        self.last_log_time = time.time()
        self.last_memory_log_time = time.time()
        
    def on_step_end(self, args, state, control, **kwargs):
        # Log every 50 steps or every 5 minutes, whichever comes first
        current_time = time.time()
        
        # Log loss every 50 steps or 5 minutes
        if (state.global_step % 50 == 0) or (current_time - self.last_log_time > 300):
            if state.log_history:
                loss = state.log_history[-1].get('loss', 'N/A')
                # Use simple formatting for better HF Space log compatibility
                log_info(f"Step {state.global_step}: Loss {loss}")
            else:
                log_info(f"Step {state.global_step}: No loss data available")
            self.last_log_time = current_time
        
        # Log memory usage every 15 minutes
        if current_time - self.last_memory_log_time > 900:  # 15 minutes
            if torch.cuda.is_available():
                memory_info = []
                for i in range(torch.cuda.device_count()):
                    allocated = torch.cuda.memory_allocated(i) / 1024**2
                    reserved = torch.cuda.memory_reserved(i) / 1024**2
                    memory_info.append(f"GPU {i}: {allocated:.1f}MB/{reserved:.1f}MB")
                
                # Log in compact format for better visibility
                log_info(f"Memory usage - {', '.join(memory_info)}")
            self.last_memory_log_time = current_time
            
    def on_train_begin(self, args, state, control, **kwargs):
        log_info("=== Training is starting ===")
        
        # Log important training parameters for visibility
        log_info(f"Batch size: {args.per_device_train_batch_size} × {args.gradient_accumulation_steps} steps × {max(1, torch.cuda.device_count())} GPUs")
        log_info(f"Learning rate: {args.learning_rate}")
        log_info(f"Epochs: {args.num_train_epochs}")
        
        # Log memory information in compact format
        if torch.cuda.is_available():
            memory_info = []
            for i in range(torch.cuda.device_count()):
                allocated = torch.cuda.memory_allocated(i) / 1024**2
                max_mem = torch.cuda.max_memory_allocated(i) / 1024**2
                memory_info.append(f"GPU {i}: {allocated:.1f}MB (max: {max_mem:.1f}MB)")
            
            log_info(f"Initial memory usage - {', '.join(memory_info)}")
            
    def on_train_end(self, args, state, control, **kwargs):
        log_info("=== Training completed ===")
        if torch.cuda.is_available():
            memory_info = []
            for i in range(torch.cuda.device_count()):
                allocated = torch.cuda.memory_allocated(i) / 1024**2
                max_mem = torch.cuda.max_memory_allocated(i) / 1024**2
                memory_info.append(f"GPU {i}: {allocated:.1f}MB (max: {max_mem:.1f}MB)")
            
            log_info(f"Final memory usage - {', '.join(memory_info)}")
        
        log_info(f"Total steps: {state.global_step}")
        log_info(f"Final loss: {state.log_history[-1].get('loss', 'N/A') if state.log_history else 'N/A'}")

def check_dependencies():
    """Check if all required dependencies are installed."""
    missing_packages = []
    
    # Critical packages
    if not unsloth_available:
        missing_packages.append("unsloth>=2024.3")
    
    if not peft_available:
        missing_packages.append("peft>=0.9.0")
    
    # Optional packages - don't add to missing list, just log
    try:
        import flash_attn
        logger.info("flash-attn found. Flash attention will be used for faster training.")
    except ImportError:
        logger.warning("flash-attn not found. Training will work but may be slower.")
        # Don't add to missing packages since it's optional and can cause build issues
    
    # If critical packages are missing, exit with instructions
    if missing_packages:
        logger.error("Critical dependencies missing:")
        for pkg in missing_packages:
            logger.error(f"  - {pkg}")
        logger.error("Please ensure the space has these packages in requirements.txt")
        return False
    
    return True

def main():
    # Set up logging
    log_info("Starting Phi-4 fine-tuning process")
    
    # Parse arguments
    args = parse_args()
    
    # Check dependencies
    if not check_dependencies():
        logger.error("Aborting due to missing critical dependencies")
        return 1
    
    # Load environment variables
    load_env_variables()
    
    # Check if we're in distributed mode
    is_distributed = "WORLD_SIZE" in os.environ and int(os.environ.get("WORLD_SIZE", "1")) > 1
    if is_distributed:
        log_info(f"Running in distributed mode with world size: {os.environ.get('WORLD_SIZE')}")
    else:
        log_info("Running in non-distributed mode (single process)")
    
    # Load all configurations
    try:
        configs = load_configs(args.config_dir)
        
        # Extract specific configs
        if not configs:
            logger.error("Failed to load configuration")
            return 1
            
        # Verify configuration sections exist
        if "transformers" not in configs:
            logger.error("transformers_config.json not found or invalid")
            return 1
            
        if "hardware" not in configs or not configs["hardware"]:
            logger.warning("Hardware configuration section not found in transformers_config.json. Using default hardware configuration.")
            
        if "dataset" not in configs or not configs["dataset"]:
            logger.error("Dataset configuration section not found in transformers_config.json")
            return 1
            
        # Validate model configuration
        model_config = configs["transformers"]
        if not model_config.get("model", {}).get("name") and not model_config.get("model_name_or_path") and not model_config.get("model_name"):
            logger.error("Model name not specified in configuration")
            logger.error("Please ensure 'name' is specified under 'model' in transformers_config.json")
            return 1
            
        model_name = model_config.get("model", {}).get("name") or model_config.get("model_name_or_path") or model_config.get("model_name")
        log_info(f"Using model: {model_name}")
        log_info("All configurations loaded successfully")
        
        # Extract specific configs
        model_config = configs["transformers"]
        hardware_config = configs.get("hardware", {})
        dataset_config = configs["dataset"]
        
        # Apply hardware-specific settings if available
        if hardware_config:
            # Get training optimizations from hardware config
            training_opts = hardware_config.get("training_optimizations", {})
            
            # Apply batch size and gradient accumulation settings
            if training_opts.get("per_device_batch_size") and model_config.get("training"):
                batch_size = training_opts.get("per_device_batch_size")
                model_config["training"]["per_device_train_batch_size"] = batch_size
                log_info(f"Applied hardware-optimized batch size: {batch_size}")
                
            if training_opts.get("gradient_accumulation_steps") and model_config.get("training"):
                grad_steps = training_opts.get("gradient_accumulation_steps")
                model_config["training"]["gradient_accumulation_steps"] = grad_steps
                log_info(f"Applied hardware-optimized gradient accumulation: {grad_steps}")
            
            # Apply memory optimizations
            memory_opts = training_opts.get("memory_optimizations", {})
            if memory_opts.get("use_gradient_checkpointing") is not None and model_config.get("training"):
                grad_ckpt = memory_opts.get("use_gradient_checkpointing")
                model_config["training"]["gradient_checkpointing"] = grad_ckpt
                log_info(f"Applied hardware-optimized gradient checkpointing: {grad_ckpt}")
            
            # Apply system settings
            system_settings = hardware_config.get("system_settings", {})
            if system_settings.get("dataloader_num_workers") is not None:
                workers = system_settings.get("dataloader_num_workers")
                log_info(f"Using {workers} dataloader workers from hardware config")
            
            # Get distribution strategy
            multi_gpu_strategy = training_opts.get("multi_gpu_strategy", "data_parallel")
            log_info(f"Hardware config specifies {multi_gpu_strategy} for multi-GPU training")
                
    except Exception as e:
        logger.error(f"Error loading configurations: {e}")
        return 1
    
    # Set random seed for reproducibility
    seed = model_config.get("seed", 42)
    set_seed(seed)
    log_info(f"Set random seed to {seed} for reproducibility")
    
    # Check CUDA and set environment variables for better memory management
    if torch.cuda.is_available():
        # Empty CUDA cache
        torch.cuda.empty_cache()
        
        # Set memory management env vars for better fragmentation handling
        os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128,expandable_segments:True"
        
        # Get memory fraction from hardware config
        cuda_memory_fraction = hardware_config.get("system_settings", {}).get("cuda_memory_fraction", 0.85)
        
        # Log initial memory information in a compact form
        gpu_info = []
        for i in range(torch.cuda.device_count()):
            name = torch.cuda.get_device_name(i)
            allocated = torch.cuda.memory_allocated(i) / 1024**3
            total = torch.cuda.get_device_properties(i).total_memory / 1024**3
            reserved_memory = total * cuda_memory_fraction
            gpu_info.append(f"GPU {i}: {name} ({allocated:.1f}GB/{reserved_memory:.1f}GB)")
        
        log_info(f"Hardware: {torch.cuda.device_count()} GPUs detected")
        log_info(f"GPU details: {', '.join(gpu_info)}")
    else:
        log_info("No GPU detected, using CPU (training will be very slow)")
    
    try:
        log_info("Loading model and tokenizer...")
        model, tokenizer = load_model_and_tokenizer(model_config)
        log_info("Model and tokenizer loaded successfully")
        
        # Load dataset with proper mapping
        try:
            log_info(f"Loading dataset from {dataset_config.get('dataset', {}).get('name', '')}")
            dataset = load_dataset_with_mapping(dataset_config)
            log_info(f"Dataset loaded with {len(dataset)} examples")
        except Exception as e:
            logger.error(f"Error loading dataset: {e}")
            return 1
        
        # Create data collator
        data_collator = SimpleDataCollator(tokenizer, dataset_config)
        
        # Verify precision settings - ensure only one of bf16/fp16 is set, with bf16 taking precedence
        # First check hardware config, then transformers config
        use_bf16 = False
        use_fp16 = False
        
        # Check hardware config first
        hardware_precision = hardware_config.get("training_optimizations", {}).get("mixed_precision", "")
        if hardware_precision.lower() == "bf16":
            use_bf16 = True
            log_info("Using BF16 precision from hardware config")
        elif hardware_precision.lower() == "fp16":
            use_fp16 = True
            log_info("Using FP16 precision from hardware config")
        else:
            # Fall back to transformers config
            use_bf16 = model_config.get("bf16", False) or model_config.get("torch_dtype", "") == "bfloat16"
            use_fp16 = model_config.get("fp16", False) and not use_bf16  # Only use fp16 if bf16 is not set
            log_info(f"Using precision: {'bf16' if use_bf16 else 'fp16' if use_fp16 else 'full precision'}")
        
        # Get per device batch size - from transformers config, but possibly overridden by hardware config
        per_device_batch_size = model_config.get("training", {}).get("per_device_train_batch_size", 16)
        gradient_accumulation_steps = model_config.get("training", {}).get("gradient_accumulation_steps", 3)
        
        # For multi-GPU setup, adjust for better balance
        if torch.cuda.device_count() > 1:
            log_info(f"Multi-GPU setup with {torch.cuda.device_count()} GPUs")
            log_info(f"Training config: {per_device_batch_size} samples/GPU × {gradient_accumulation_steps} accumulation steps")
        
        # Determine multi-GPU strategy from hardware config
        multi_gpu_strategy = hardware_config.get("training_optimizations", {}).get("multi_gpu_strategy", "data_parallel")
        
        # Set up FSDP for multi-GPU training if specified and in distributed mode
        fsdp_config = None
        if multi_gpu_strategy == "fsdp" and is_distributed and torch.cuda.device_count() > 1:
            try:
                from torch.distributed.fsdp import (
                    FullyShardedDataParallel as FSDP,
                    MixedPrecision,
                    BackwardPrefetch,
                    ShardingStrategy,
                    CPUOffload,
                )
                from torch.distributed.fsdp.wrap import (
                    transformer_auto_wrap_policy,
                    enable_wrap,
                    wrap,
                )
                
                log_info("Using FSDP for distributed training")
                
                # Configure FSDP
                fsdp_config = {
                    "fsdp_transformer_layer_cls_to_wrap": ["LlamaDecoderLayer"],
                    "fsdp_offload_params": False,
                    "fsdp_backward_prefetch": "BACKWARD_PRE",
                    "fsdp_min_num_params": 1e6,
                    "fsdp_sharding_strategy": 1,  # FULL_SHARD
                }
                
                if use_bf16 or use_fp16:
                    precision_type = "bf16" if use_bf16 else "fp16"
                    fsdp_config["fsdp_state_dict_type"] = "FULL_STATE_DICT"
                    log_info(f"FSDP using mixed precision: {precision_type}")
            except ImportError:
                log_info("FSDP imports failed, falling back to standard DDP")
                fsdp_config = None
        elif multi_gpu_strategy == "fsdp" and not is_distributed:
            log_info("FSDP disabled: requires distributed environment (use torchrun or accelerate)")
            log_info("Using DataParallel for multi-GPU training instead")
        else:
            log_info(f"Using {multi_gpu_strategy} for multi-GPU training")
        
        # Get system settings from hardware config
        dataloader_workers = hardware_config.get("system_settings", {}).get("dataloader_num_workers", 2)
        pin_memory = hardware_config.get("system_settings", {}).get("dataloader_pin_memory", True)
        
        # Set up training arguments
        log_info("Setting up training arguments")
        training_args = TrainingArguments(
            output_dir=model_config.get("output_dir", "./results") or model_config.get("checkpointing", {}).get("output_dir", "./results"),
            num_train_epochs=model_config.get("training", {}).get("num_train_epochs", 3),
            per_device_train_batch_size=per_device_batch_size,
            gradient_accumulation_steps=gradient_accumulation_steps,
            learning_rate=model_config.get("training", {}).get("learning_rate", 2e-5),
            weight_decay=model_config.get("training", {}).get("weight_decay", 0.01),
            warmup_ratio=model_config.get("training", {}).get("warmup_ratio", 0.05),
            lr_scheduler_type=model_config.get("training", {}).get("lr_scheduler_type", "cosine"),
            logging_steps=model_config.get("training", {}).get("logging_steps", 10),
            save_strategy=model_config.get("checkpointing", {}).get("save_strategy", "steps"),
            save_steps=model_config.get("checkpointing", {}).get("save_steps", 100),
            save_total_limit=model_config.get("checkpointing", {}).get("save_total_limit", 3),
            fp16=use_fp16,
            bf16=use_bf16,
            max_grad_norm=model_config.get("training", {}).get("max_grad_norm", 1.0),
            push_to_hub=model_config.get("huggingface_hub", {}).get("push_to_hub", False),
            hub_model_id=model_config.get("huggingface_hub", {}).get("hub_model_id", None),
            hub_token=os.environ.get("HF_TOKEN", None),
            report_to="tensorboard",
            remove_unused_columns=False,  # Keep all columns
            gradient_checkpointing=model_config.get("training", {}).get("gradient_checkpointing", True),
            dataloader_pin_memory=pin_memory,
            optim=model_config.get("training", {}).get("optim", "adamw_torch"),
            ddp_find_unused_parameters=False,  # Improve distributed training efficiency
            dataloader_drop_last=False,  # Process all examples
            dataloader_num_workers=dataloader_workers,
            no_cuda=False if torch.cuda.is_available() else True,  # Use CUDA if available
            # Only add FSDP if we're in distributed mode with FSDP strategy
            fsdp=fsdp_config if is_distributed and multi_gpu_strategy == "fsdp" else None,
        )
        
        # Create sequential sampler to maintain original dataset order
        sequential_sampler = torch.utils.data.SequentialSampler(dataset)
        
        # Initialize trainer first
        log_info("Initializing Trainer")
        trainer = Trainer(
            model=model,
            args=training_args,
            train_dataset=dataset,  # We'll override this with our custom dataloader
            data_collator=data_collator,
            callbacks=[LoggingCallback()],
        )
        
        # Then override the get_train_dataloader method
        def custom_get_train_dataloader():
            """Custom dataloader that preserves original dataset order"""
            log_info("Creating sequential dataloader to maintain original dataset order")
            
            # Calculate batch size based on device availability
            if getattr(training_args, "no_cuda", False):
                batch_size = training_args.per_device_train_batch_size
            else:
                batch_size = max(training_args.per_device_train_batch_size * max(1, torch.cuda.device_count()), 1)
                
            log_info(f"Using sequential sampler with batch size {batch_size}")
            
            # Return DataLoader with sequential sampler
            return torch.utils.data.DataLoader(
                dataset,
                batch_size=batch_size,
                sampler=sequential_sampler,
                collate_fn=data_collator,
                drop_last=training_args.dataloader_drop_last,
                num_workers=training_args.dataloader_num_workers,
                pin_memory=training_args.dataloader_pin_memory,
            )
        
        # Override the get_train_dataloader method
        trainer.get_train_dataloader = custom_get_train_dataloader
        
        # Start training
        log_info("=== Starting Training ===")
        try:
            # Empty cache again right before training
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
                log_info("Cleared CUDA cache before training")
            
            # Display compact training info
            total_steps = int(len(dataset) / (per_device_batch_size * torch.cuda.device_count() * gradient_accumulation_steps) * training_args.num_train_epochs)
            log_info(f"Training plan: {len(dataset)} examples over {training_args.num_train_epochs} epochs ≈ {total_steps} steps")
            
            trainer.train()
            log_info("Training completed successfully!")
            
            # Save the final model
            log_info("Saving final model...")
            trainer.save_model()
            log_info(f"Model saved to {training_args.output_dir}")
            
            # Push to hub if enabled
            if model_config.get("huggingface_hub", {}).get("push_to_hub", False):
                hub_id = model_config.get("huggingface_hub", {}).get("hub_model_id", "model")
                log_info(f"Pushing model to Hugging Face Hub as {hub_id}...")
                trainer.push_to_hub()
                log_info("Model successfully pushed to Hub")
                
            return 0
        except Exception as e:
            logger.error(f"Training failed with error: {str(e)}")
            # Log CUDA memory info if available in compact format
            if torch.cuda.is_available():
                memory_info = []
                for i in range(torch.cuda.device_count()):
                    allocated = torch.cuda.memory_allocated(i) / 1024**2
                    reserved = torch.cuda.memory_reserved(i) / 1024**2
                    max_mem = torch.cuda.max_memory_allocated(i) / 1024**2
                    memory_info.append(f"GPU {i}: {allocated:.1f}MB/{reserved:.1f}MB (max: {max_mem:.1f}MB)")
                logger.error(f"GPU memory at failure: {', '.join(memory_info)}")
            raise
    
    except Exception as e:
        logger.error(f"Error in main training loop: {str(e)}")
        return 1

if __name__ == "__main__":
    sys.exit(main())