[fix] data skip

trainer/train_distillation.py

@@ -222,7 +222,7 @@ if __name__ == "__main__":
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
-        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: 

trainer/train_dpo.py

@@ -206,7 +206,7 @@ if __name__ == "__main__":
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
-        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: 

trainer/train_full_sft.py

@@ -149,7 +149,7 @@ if __name__ == "__main__":
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
-        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: 

trainer/train_grpo.py

@@ -291,7 +291,7 @@ if __name__ == "__main__":
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
-        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: 

trainer/train_lora.py

@@ -162,7 +162,7 @@ if __name__ == "__main__":
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
-        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: 

trainer/train_ppo.py

@@ -366,7 +366,7 @@ if __name__ == "__main__":
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
-        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: 

trainer/train_pretrain.py

@@ -148,7 +148,7 @@ if __name__ == "__main__":
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
-        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: 

trainer/train_reason.py

@@ -166,7 +166,7 @@ if __name__ == "__main__":
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
-        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: 

trainer/train_spo.py

@@ -341,7 +341,7 @@ if __name__ == "__main__":
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
-        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        skip = start_step if (epoch == start_epoch and start_step > 0) else 0
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: