[QEff. Finetune]: Correcting num_steps trained as per max_train_step and displaying non scaled loss value on console. (#527)

Signed-off-by: Swati Allabadi <sallabad@qti.qualcomm.com>
Co-authored-by: Swati Allabadi <sallabad@qti.qualcomm.com>

Author: quic-swatia

QEfficient/finetune/utils/train_utils.py

@@ -124,10 +124,9 @@ def train(
 
         if train_config.use_peft and train_config.from_peft_checkpoint:
             intermediate_epoch = int(train_config.from_peft_checkpoint.split("/")[-2].split("_")[-1]) - 1
+            intermediate_step = int(train_config.from_peft_checkpoint.split("/")[-1].split("_")[-1])
             if epoch < intermediate_epoch:
                 logger.log_rank_zero(f"Skipping epoch {epoch + 1} since fine tuning has already completed for it.")
-                # to bring the count of train_step in sync with where it left off
-                total_train_steps += len(train_dataloader)
                 continue
 
         logger.log_rank_zero(f"Starting epoch {epoch + 1}/{train_config.num_epochs}")
@@ -149,20 +148,18 @@ def train(
 
         num_dummy_samples = 0
         for step, batch in enumerate(train_dataloader):
+            # total_train_steps indicates the cumulative number of training steps completed across all epochs.
+            # When resuming fine-tuning from previously saved checkpoints, total_train_steps indicates the total number of steps trained across the earlier session and the ongoing one.
+            total_train_steps = (epoch) * len(train_dataloader) + step
             # resume training from a particular checkpoint, assuming the dataset is not shuffled
             if train_config.use_peft and train_config.from_peft_checkpoint:
-                intermediate_step = int(train_config.from_peft_checkpoint.split("/")[-1].split("_")[-1])
-                intermediate_epoch = int(train_config.from_peft_checkpoint.split("/")[-2].split("_")[-1]) - 1
                 # to bring the count of train_step in sync with where it left off
                 if epoch == intermediate_epoch and step == 0:
-                    total_train_steps += intermediate_step
                     logger.log_rank_zero(
                         f"Skipping first {intermediate_step} steps for epoch {epoch + 1}, since fine tuning has already completed for it."
                     )
                 if epoch == intermediate_epoch and step < intermediate_step:
-                    total_train_steps += 1
                     continue
-            total_train_steps += 1
 
             if train_config.max_train_step > 0 and total_train_steps >= train_config.max_train_step:
                 max_steps_reached = True
@@ -235,12 +232,12 @@ def train(
             else:
                 num_samples_in_cur_update = len(train_dataloader) % train_config.gradient_accumulation_steps
 
-            loss = loss / num_samples_in_cur_update
+            normalized_loss = loss / num_samples_in_cur_update
 
             if train_config.grad_scaler:
-                scaler.scale(loss).backward()  # backward pass
+                scaler.scale(normalized_loss).backward()  # backward pass
             else:
-                loss.backward()  # backward pass
+                normalized_loss.backward()  # backward pass
 
             if is_optimizer_step:
                 if train_config.grad_scaler: