solves oom error with more reasonable configuration

config/train_smollm3_openhermes_fr_a100_balanced.py

@@ -0,0 +1,164 @@
+"""
+SmolLM3 Training Configuration for OpenHermes-FR Dataset - A100 Balanced
+Optimized for good GPU utilization without running out of memory
+"""
+
+import os
+from dataclasses import dataclass
+from typing import Optional
+from config.train_smollm3 import SmolLM3Config
+
+@dataclass
+class SmolLM3ConfigOpenHermesFRBalanced(SmolLM3Config):
+    """Configuration for SmolLM3 fine-tuning with balanced A100 performance"""
+    
+    # Model configuration - balanced for A100
+    model_name: str = "HuggingFaceTB/SmolLM3-3B"
+    max_seq_length: int = 12288  # Increased but not too much
+    use_flash_attention: bool = True
+    use_gradient_checkpointing: bool = False  # Disabled for A100 efficiency
+    
+    # Training configuration - Balanced GPU utilization
+    batch_size: int = 8  # Moderate increase
+    gradient_accumulation_steps: int = 16  # Effective batch size = 8 * 16 = 128
+    learning_rate: float = 3.5e-6  # Slightly higher for larger effective batch
+    weight_decay: float = 0.01
+    warmup_steps: int = 1200  # More warmup for larger batch
+    max_iters: int = 18000  # More iterations for faster convergence
+    eval_interval: int = 1000  # Less frequent evaluation
+    log_interval: int = 25  # Less frequent logging
+    save_interval: int = 2000  # Less frequent saving
+    
+    # Optimizer configuration - optimized for large batches
+    optimizer: str = "adamw_torch"
+    beta1: float = 0.9
+    beta2: float = 0.999  # Higher beta2 for stability
+    eps: float = 1e-8
+    
+    # Scheduler configuration - faster training
+    scheduler: str = "cosine"
+    min_lr: float = 3.5e-7  # Lower min LR
+    
+    # Mixed precision - A100 optimized
+    fp16: bool = False  # Use bf16 for A100
+    bf16: bool = True  # Better for A100
+    
+    # DDP configuration
+    ddp_backend: str = "nccl"
+    ddp_find_unused_parameters: bool = False
+    
+    # Logging and saving - optimized for fast training
+    save_steps: int = 2000
+    eval_steps: int = 1000
+    logging_steps: int = 25
+    save_total_limit: Optional[int] = 5  # Keep fewer checkpoints
+    
+    # Evaluation
+    eval_strategy: str = "steps"
+    metric_for_best_model: str = "eval_loss"
+    greater_is_better: bool = False
+    load_best_model_at_end: bool = True
+    
+    # OpenHermes-FR Dataset configuration
+    dataset_name: str = "legmlai/openhermes-fr"
+    dataset_split: str = "train"
+    input_field: str = "prompt"
+    target_field: str = "accepted_completion"
+    filter_bad_entries: bool = True
+    bad_entry_field: str = "bad_entry"
+    
+    # Data configuration (not used for HF datasets but kept for compatibility)
+    data_dir: str = None
+    train_file: str = None
+    validation_file: Optional[str] = None
+    test_file: Optional[str] = None
+    
+    # Chat template configuration
+    use_chat_template: bool = True
+    chat_template_kwargs: dict = None
+    
+    # Trackio monitoring configuration
+    enable_tracking: bool = True
+    trackio_url: Optional[str] = None
+    trackio_token: Optional[str] = None
+    log_artifacts: bool = True
+    log_metrics: bool = True
+    log_config: bool = True
+    experiment_name: Optional[str] = None
+    
+    # Additional A100 optimizations for balanced performance
+    dataloader_num_workers: int = 10  # More workers for faster data loading
+    dataloader_pin_memory: bool = True
+    dataloader_prefetch_factor: int = 3  # Increased prefetch
+    
+    # Memory optimizations
+    max_grad_norm: float = 1.0  # Gradient clipping
+    group_by_length: bool = True  # Group similar length sequences
+    
+    # Training duration calculations
+    # With 800k datapoints and effective batch size of 128:
+    # Steps per epoch = 800,000 / 128 = 6,250 steps
+    # For 3 passes: 6,250 * 3 = 18,750 steps
+    # Current max_iters = 18,000 (about 2.9 passes)
+    
+    def __post_init__(self):
+        if self.chat_template_kwargs is None:
+            self.chat_template_kwargs = {
+                "enable_thinking": False,
+                "add_generation_prompt": True
+            }
+        
+        # Validate configuration
+        if self.fp16 and self.bf16:
+            raise ValueError("Cannot use both fp16 and bf16")
+        
+        if self.max_seq_length > 131072:  # 128k limit
+            raise ValueError("max_seq_length cannot exceed 131072")
+        
+        # Calculate training statistics
+        effective_batch_size = self.batch_size * self.gradient_accumulation_steps
+        steps_per_epoch = 800000 // effective_batch_size  # Approximate for 800k dataset
+        epochs_for_max_iters = self.max_iters / steps_per_epoch
+        
+        print(f"=== A100 Balanced Configuration ===")
+        print(f"Effective batch size: {effective_batch_size}")
+        print(f"Steps per epoch: ~{steps_per_epoch}")
+        print(f"Training for ~{epochs_for_max_iters:.1f} epochs")
+        print(f"Total training steps: {self.max_iters}")
+        print(f"Learning rate: {self.learning_rate}")
+        print(f"Mixed precision: {'bf16' if self.bf16 else 'fp16'}")
+        print(f"Max sequence length: {self.max_seq_length}")
+        print(f"Gradient checkpointing: {self.use_gradient_checkpointing}")
+        print(f"Batch size: {self.batch_size}")
+        print(f"Gradient accumulation: {self.gradient_accumulation_steps}")
+        print(f"Data loader workers: {self.dataloader_num_workers}")
+        print(f"Prefetch factor: {self.dataloader_prefetch_factor}")
+        print("=" * 50)
+        
+        # Set default experiment name if not provided
+        if self.experiment_name is None:
+            self.experiment_name = "smollm3_openhermes_fr_balanced"
+
+def get_config(config_path: str) -> SmolLM3ConfigOpenHermesFRBalanced:
+    """Load configuration from file or return default"""
+    if os.path.exists(config_path):
+        # Load from file if it exists
+        import importlib.util
+        spec = importlib.util.spec_from_file_location("config_module", config_path)
+        config_module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(config_module)
+        
+        if hasattr(config_module, 'config'):
+            return config_module.config
+        else:
+            # Try to find a config class
+            for attr_name in dir(config_module):
+                attr = getattr(config_module, attr_name)
+                if isinstance(attr, SmolLM3ConfigOpenHermesFRBalanced):
+                    return attr
+    
+    # Return default configuration
+    return SmolLM3ConfigOpenHermesFRBalanced()
+
+# Default configuration instance
+config = SmolLM3ConfigOpenHermesFRBalanced() 

run_a100_large_experiment.py

@@ -9,6 +9,9 @@ import os
 import sys
 from pathlib import Path
 
+# Set CUDA memory optimization
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+
 def main():
     parser = argparse.ArgumentParser(description="Run A100 large-scale experiments")
     parser.add_argument(