Fix bug in examples: double wrap into DataParallel during eval
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@ -255,7 +255,7 @@ def evaluate(args, model, tokenizer, prefix=""):
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eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
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# multi-gpu eval
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if args.n_gpu > 1:
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if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
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model = torch.nn.DataParallel(model)
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# Eval!
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@ -278,7 +278,7 @@ def evaluate(args, model, tokenizer, criterion, prefix=""):
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)
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# multi-gpu eval
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if args.n_gpu > 1:
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if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
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model = torch.nn.DataParallel(model)
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# Eval!
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@ -253,7 +253,7 @@ def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""
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eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
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# multi-gpu evaluate
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if args.n_gpu > 1:
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if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
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model = torch.nn.DataParallel(model)
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# Eval!
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@ -427,7 +427,7 @@ def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefi
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)
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# multi-gpu evaluate
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if args.n_gpu > 1:
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if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
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model = torch.nn.DataParallel(model)
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# Eval!
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@ -256,7 +256,7 @@ def evaluate(args, model, tokenizer, prefix="", test=False):
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eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
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# multi-gpu evaluate
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if args.n_gpu > 1:
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if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
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model = torch.nn.DataParallel(model)
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# Eval!
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@ -266,7 +266,7 @@ def evaluate(args, model, tokenizer, prefix=""):
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eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
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# multi-gpu eval
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if args.n_gpu > 1:
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if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
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model = torch.nn.DataParallel(model)
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# Eval!
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