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@@ -89,7 +89,10 @@ def train(model, train_dataloader,eval_dataloader, tokenizer, optimizer, lr_sche
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if train_config.enable_fsdp:
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batch[key] = batch[key].to(local_rank)
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else:
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- batch[key] = batch[key].to('cuda:0')
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+ if is_xpu_available():
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+ batch[key] = batch[key].to('xpu:0')
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+ else:
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+ batch[key] = batch[key].to('cuda:0')
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loss = model(**batch).loss
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loss = loss / gradient_accumulation_steps
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total_loss += loss.detach().float()
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@@ -260,7 +263,10 @@ def evaluation(model,train_config, eval_dataloader, local_rank, tokenizer):
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if train_config.enable_fsdp:
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batch[key] = batch[key].to(local_rank)
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else:
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- batch[key] = batch[key].to('cuda:0')
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+ if is_xpu_available():
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+ batch[key] = batch[key].to('xpu:0')
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+ else:
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+ batch[key] = batch[key].to('cuda:0')
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# Ensure no gradients are computed for this scope to save memory
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with torch.no_grad():
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# Forward pass and compute loss
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@@ -274,7 +280,7 @@ def evaluation(model,train_config, eval_dataloader, local_rank, tokenizer):
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)
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# If there's more than one CUDA device, reduce evaluation loss across all devices
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- if is_xpu_available() and (torch.cuda.device_count() > 1 and train_config.enable_fsdp):
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+ if is_xpu_available() and (torch.xpu.device_count() > 1 and train_config.enable_fsdp):
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dist.all_reduce(eval_loss, op=dist.ReduceOp.SUM)
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if torch.cuda.device_count() > 1 and train_config.enable_fsdp:
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dist.all_reduce(eval_loss, op=dist.ReduceOp.SUM)
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abhilash1910