Implement plan.md: Loss scaling, LR=5e-5, Step-logging

local_train.py

@@ -33,14 +33,14 @@ def main():
         "--data_path", DATASET_DIR,
         "--epochs", "5",
         "--batch_size", "64",
-        "--lr", "1e-4",
+        "--lr", "5e-5",
         "--min_lr", "5e-6",
         "--num_workers", "8",  # 0 for local windows debugging usually safer
         "--esm_model_name", "facebook/esm2_t6_8M_UR50D",
         "--use_lora", "True",
         "--lora_rank", "8",
         # Asymmetric Loss defaults
-        "--gamma_neg", "4",
+        "--gamma_neg", "2",
         "--gamma_pos", "0",
         "--clip", "0.05",
         "--max_grad_norm", "1.0",

src/model.py

@@ -202,7 +202,8 @@ class AsymmetricLoss(nn.Module):
         loss = - (los_pos + los_neg)
         
         if self.reduction == 'mean':
-            loss = loss.mean()
+            # loss = loss.mean()
+            loss = loss.sum() / x.size(0)
         elif self.reduction == 'sum':
             loss = loss.sum()
             

src/train.py

@@ -319,7 +319,7 @@ def validate_loss(model, valid_loader, criterion, device):
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--data_path", type=str, required=True, help="Path to mounted dataset")
-    parser.add_argument("--lr", type=float, default=1e-4)
+    parser.add_argument("--lr", type=float, default=5e-5)
     parser.add_argument("--batch_size", type=int, default=32)
     parser.add_argument("--epochs", type=int, default=10)
     parser.add_argument("--num_workers", type=int, default=4, help="Number of data loader workers")
@@ -327,7 +327,7 @@ def main():
     parser.add_argument("--T_mult", type=int, default=1, help="CosineAnnealingWarmRestarts T_mult")
     parser.add_argument("--min_lr", type=float, default=1e-6, help="Minimum learning rate")
     parser.add_argument("--esm_model_name", type=str, default="facebook/esm2_t33_650M_UR50D", help="ESM model name")
-    parser.add_argument("--gamma_neg", type=float, default=4, help="Asymmetric Loss gamma_neg")
+    parser.add_argument("--gamma_neg", type=float, default=2, help="Asymmetric Loss gamma_neg")
     parser.add_argument("--gamma_pos", type=float, default=0, help="Asymmetric Loss gamma_pos")
     parser.add_argument("--clip", type=float, default=0.05, help="Asymmetric Loss clip")
     parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping")
@@ -542,6 +542,18 @@ def main():
             total_loss += loss.item()
             total_grad_norm += grad_norm.item()
             steps += 1
+            
+            # Step-wise Logging
+            if steps % 10 == 0:
+                current_gnorm = grad_norm.item() if isinstance(grad_norm, torch.Tensor) else grad_norm
+                global_step = (epoch - 1) * len(train_loader) + steps
+                
+                mlflow.log_metrics({
+                    "step_train_loss": loss.item(),
+                    "step_grad_norm": current_gnorm,
+                    "step_lr": optimizer.param_groups[0]['lr']
+                }, step=global_step)
+
             pbar.set_postfix({'loss': total_loss/steps})
             
             if args.dry_run and steps >= 5: