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@@ -69,7 +69,7 @@ def train(model, train_dataloader,eval_dataloader, tokenizer, optimizer, lr_sche
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model.train()
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total_loss = 0.0
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total_length = len(train_dataloader)//gradient_accumulation_steps
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- pbar = tqdm(colour="blue", desc=f"Training Epoch: {epoch+1}", total=total_length)
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+ pbar = tqdm(colour="blue", desc=f"Training Epoch: {epoch+1}", total=total_length, dynamic_ncols=True)
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for step, batch in enumerate(train_dataloader):
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for key in batch.keys():
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if train_config.enable_fsdp:
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@@ -227,7 +227,7 @@ def evaluation(model,train_config, eval_dataloader, local_rank, tokenizer):
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eval_preds = []
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eval_loss = 0.0 # Initialize evaluation loss
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with MemoryTrace() as memtrace:
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- for step, batch in enumerate(tqdm(eval_dataloader,colour="green", desc="evaluating Epoch")):
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+ for step, batch in enumerate(tqdm(eval_dataloader,colour="green", desc="evaluating Epoch", dynamic_ncols=True)):
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for key in batch.keys():
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if train_config.enable_fsdp:
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batch[key] = batch[key].to(local_rank)
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