adds max perf script

config/__init__.py

@@ -6,6 +6,7 @@ from .train_smollm3 import SmolLM3Config, get_config as get_base_config
 from .train_smollm3_openhermes_fr import SmolLM3ConfigOpenHermesFR, get_config as get_openhermes_fr_config
 from .train_smollm3_openhermes_fr_a100_large import SmolLM3ConfigOpenHermesFRA100Large, get_config as get_a100_large_config
 from .train_smollm3_openhermes_fr_a100_multiple_passes import SmolLM3ConfigOpenHermesFRMultiplePasses, get_config as get_multiple_passes_config
+from .train_smollm3_openhermes_fr_a100_max_performance import SmolLM3ConfigOpenHermesFRMaxPerformance, get_config as get_max_performance_config
 
 # Generic get_config function that can handle different config types
 def get_config(config_path: str):
@@ -20,6 +21,8 @@ def get_config(config_path: str):
         return get_a100_large_config(config_path)
     elif "a100_multiple_passes" in config_path:
         return get_multiple_passes_config(config_path)
+    elif "a100_max_performance" in config_path:
+        return get_max_performance_config(config_path)
     elif "openhermes_fr" in config_path:
         return get_openhermes_fr_config(config_path)
     else:
@@ -30,9 +33,11 @@ __all__ = [
     'SmolLM3ConfigOpenHermesFR', 
     'SmolLM3ConfigOpenHermesFRA100Large',
     'SmolLM3ConfigOpenHermesFRMultiplePasses',
+    'SmolLM3ConfigOpenHermesFRMaxPerformance',
     'get_config',
     'get_base_config',
     'get_openhermes_fr_config',
     'get_a100_large_config',
     'get_multiple_passes_config',
+    'get_max_performance_config',
 ] 

config/train_smollm3_openhermes_fr_a100_max_performance.py

@@ -0,0 +1,165 @@
+"""
+SmolLM3 Training Configuration for OpenHermes-FR Dataset - A100 Max Performance
+Optimized for maximum GPU utilization and fastest training on A100
+"""
+
+import os
+from dataclasses import dataclass
+from typing import Optional
+from config.train_smollm3 import SmolLM3Config
+
+@dataclass
+class SmolLM3ConfigOpenHermesFRMaxPerformance(SmolLM3Config):
+    """Configuration for SmolLM3 fine-tuning with maximum A100 performance"""
+    
+    # Model configuration - optimized for A100
+    model_name: str = "HuggingFaceTB/SmolLM3-3B"
+    max_seq_length: int = 16384  # Increased for better GPU utilization
+    use_flash_attention: bool = True
+    use_gradient_checkpointing: bool = False  # Disabled for A100 efficiency
+    
+    # Training configuration - Maximum GPU utilization
+    batch_size: int = 12  # Increased batch size for A100
+    gradient_accumulation_steps: int = 12  # Effective batch size = 12 * 12 = 144
+    learning_rate: float = 4e-6  # Slightly higher for larger effective batch
+    weight_decay: float = 0.01
+    warmup_steps: int = 1500  # More warmup for larger batch
+    max_iters: int = 20000  # 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 = 4e-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 maximum performance
+    dataloader_num_workers: int = 12  # More workers for faster data loading
+    dataloader_pin_memory: bool = True
+    dataloader_prefetch_factor: int = 4  # 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 144:
+    # Steps per epoch = 800,000 / 144 = 5,556 steps
+    # For 3 passes: 5,556 * 3 = 16,667 steps
+    # For 4 passes: 5,556 * 4 = 22,222 steps
+    # Current max_iters = 20,000 (about 3.6 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 Max Performance 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_max_performance"
+
+def get_config(config_path: str) -> SmolLM3ConfigOpenHermesFRMaxPerformance:
+    """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, SmolLM3ConfigOpenHermesFRMaxPerformance):
+                    return attr
+    
+    # Return default configuration
+    return SmolLM3ConfigOpenHermesFRMaxPerformance()
+
+# Default configuration instance
+config = SmolLM3ConfigOpenHermesFRMaxPerformance()