Update config/baseline_config.py

config/baseline_config.py

@@ -1,39 +1,43 @@
 """
-Configuration for baseline transformer on enwik8.
-Matches DTAT config exactly for fair comparison.
+Configuration for Baseline Transformer on enwik8.
+Matches DTAT's training setup for fair comparison.
 """
 
 class BaselineConfig:
     def __init__(self):
         # Model architecture (exactly matching DTAT)
         self.n_layer = 12
-        self.n_head = 8
-        self.n_embd = 512
+        self.n_head = 8  # Same as DTAT
+        self.n_embd = 512  # Same as DTAT
         self.dropout = 0.1
         self.bias = True
         
         # Sequence parameters
-        self.block_size = 1024
+        self.block_size = 1024  # Same as DTAT
         self.vocab_size = 256  # For character-level model
         
-        # Training parameters
+        # Training parameters (matched with DTAT)
         self.learning_rate = 6e-4
-        self.min_lr = 6e-5
-        self.warmup_iters = 733     # 5% of 14,667 iterations
-        self.max_iters = 14667      # Exactly 4 epochs with batch_size=24
-        self.weight_decay = 1e-1
+        self.min_lr = 1e-5         # Lower minimum to allow fine-tuning
+        self.warmup_iters = 367     # 5% of total iterations
+        self.max_iters = 7334      # Exactly 4 epochs with batch_size=24
+        self.weight_decay = 0.1     # Same as DTAT
         self.beta1 = 0.9
         self.beta2 = 0.95
         self.grad_clip = 1.0
         
         # Learning rate schedule
         self.decay_lr = True
-        self.lr_decay_iters = self.max_iters  # Full schedule
+        self.lr_decay_iters = 5000  # Same as DTAT
+        
+        # Early stopping
+        self.patience = 15          # Same as DTAT
+        self.min_delta = 0.005     # Same as DTAT
+        self.eval_interval = 250    # Same as DTAT
+        self.eval_iters = 200      # Same as DTAT
         
         # Logging
         self.log_interval = 10
-        self.eval_interval = 500
-        self.eval_iters = 200
         
         # Mixed precision training
         self.mixed_precision = True
@@ -46,9 +50,18 @@ class BaselineConfig:
         # System
         self.device = 'cuda'
         self.compile = True
+        
+        # Performance optimization
+        self.compile_model = True
+        self.cudnn_benchmark = True
+        
+        # Git config for model versioning
+        self.git_name = "Your Name"
+        self.git_email = "your.email@example.com"
     
     def get_config(self):
         return self
 
 def get_config():
+    """Helper function to get config instance."""
     return BaselineConfig()