Fix a few issues found during fine-tuning (#2582)

* Fix wandb log

* fix validation loss is not logged

batch_idx got reset for each epoch.
use the global step counter instead

* LR should only be updated per step, not per step+ per epoch

* add early stopping

* Fix bf16 handling

scaler is only needed for fp16

* more logs

---------

Co-authored-by: Tony Mak <tony@Tonys-MacBook-Air-1800.local>

funasr/bin/train_ds.py

@@ -149,7 +149,7 @@ def main(**kwargs):
     dataloader = dataloader_class(**kwargs)
     # dataloader_tr, dataloader_val = dataloader_class(**kwargs)
 
-    scaler = GradScaler(enabled=True) if trainer.use_fp16 or trainer.use_bf16 else None
+    scaler = GradScaler(enabled=True) if trainer.use_fp16 else None
     scaler = ShardedGradScaler(enabled=trainer.use_fp16) if trainer.use_fsdp else scaler
 
     trainer.resume_checkpoint(
@@ -159,6 +159,10 @@ def main(**kwargs):
         scaler=scaler,
     )
 
+    early_stopping_patience = kwargs.get("train_conf", {}).get("early_stopping_patience", 0)
+    best_val_loss = float("inf")
+    epochs_no_improve = 0
+
     dataloader_tr, dataloader_val = None, None
     for epoch in range(trainer.start_epoch, trainer.max_epoch):
         time1 = time.perf_counter()
@@ -199,7 +203,19 @@ def main(**kwargs):
 
         trainer.start_data_split_i = 0
         trainer.validate_epoch(model=model, dataloader_val=dataloader_val, epoch=epoch + 1)
-        scheduler.step()
+        current_val = trainer.val_loss_avg
+
+        if current_val < best_val_loss:
+            logging.info(f"current_val: {current_val}, best_val_loss: {best_val_loss}")
+            best_val_loss = current_val
+            epochs_no_improve = 0
+        else:
+            epochs_no_improve += 1
+            logging.info(f"No val_loss improvement for {epochs_no_improve}/{early_stopping_patience} epochs")
+        if early_stopping_patience > 0 and epochs_no_improve >= early_stopping_patience:
+            logging.info(f"Early stopping triggered at epoch {epoch+1}")
+            break
+
         trainer.step_in_epoch = 0
         trainer.save_checkpoint(
             epoch + 1, model=model, optim=optim, scheduler=scheduler, scaler=scaler

funasr/train_utils/trainer.py

@@ -715,7 +715,7 @@ class Trainer:
             if self.use_wandb and wandb is not None:
                 wandb.log(
                     description_dict,
-                    setp=self.batch_total,
+                    step=self.batch_total,
                 )
 
     def close(self, writer=None):

funasr/train_utils/trainer_ds.py

@@ -30,9 +30,8 @@ def maybe_autocast(dtype=None, use_deepspeed=False):
             yield
     else:
         if dtype == torch.float16 or dtype == torch.bfloat16:
-            yield
-            # with autocast(enabled=True, dtype=dtype):
-            #     yield
+            with autocast(enabled=True, dtype=dtype):
+                yield
         else:
             yield
 
@@ -684,7 +683,7 @@ class Trainer:
             scaled_loss = model.backward(loss)
         else:
             loss = loss / self.accum_grad
-            if self.use_fp16 or self.use_bf16:
+            if scaler:
                 scaler.scale(loss).backward()
             else:
                 loss.backward()
@@ -712,7 +711,7 @@ class Trainer:
                 # Execute an optimization step (update model parameters)
                 if self.use_ddp or self.use_fsdp:
                     dist.barrier()
-                if self.use_fp16 or self.use_bf16:
+                if scaler:
                     scaler.step(optim)
                     scaler.update()
                 else:
@@ -736,6 +735,9 @@ class Trainer:
         Args:
             epoch (int): The current epoch number.
         """
+        self.val_loss_avg = 0.0
+        self.val_acc_avg  = 0.0
+
         if self.use_ddp or self.use_fsdp or self.use_deepspeed:
             dist.barrier()
         logging.info(f"Validate epoch: {epoch}, rank: {self.rank}\n")
@@ -757,7 +759,7 @@ class Trainer:
                     "data_split_i": kwargs.get("data_split_i", 0),
                     "data_split_num": kwargs.get("data_split_num", 1),
                     "log_step": batch_idx + kwargs.get("start_step", 0),
-                    "batch_total": batch_idx + 1,
+                    "batch_total": self.batch_total,
                     "step_in_epoch": batch_idx + 1,
                     "lr": 0.0,
                 }
@@ -883,7 +885,7 @@ class Trainer:
             if self.use_wandb and wandb is not None:
                 wandb.log(
                     description_dict,
-                    setp=batch_total,
+                    step=batch_total,
                 )
 
     def close(self, writer=None):