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Fix bug in examples: double wrap into DataParallel during eval

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This commit is contained in:
Andrey Kulagin
2020-04-17 17:33:24 +03:00
committed by Julien Chaumond
parent 7f23af1684
commit b1ff0b2ae7
6 changed files with 6 additions and 6 deletions
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2
examples/hans/test_hans.py
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@@ -255,7 +255,7 @@ def evaluate(args, model, tokenizer, prefix=""):
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# multi-gpu eval # multi-gpu eval
if args.n_gpu > 1: if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model) model = torch.nn.DataParallel(model)
# Eval! # Eval!

2
examples/mm-imdb/run_mmimdb.py
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@@ -278,7 +278,7 @@ def evaluate(args, model, tokenizer, criterion, prefix=""):
) )
# multi-gpu eval # multi-gpu eval
if args.n_gpu > 1: if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model) model = torch.nn.DataParallel(model)
# Eval! # Eval!

2
examples/ner/run_ner.py
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@@ -253,7 +253,7 @@ def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# multi-gpu evaluate # multi-gpu evaluate
if args.n_gpu > 1: if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model) model = torch.nn.DataParallel(model)
# Eval! # Eval!

2
examples/run_language_modeling.py
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@@ -427,7 +427,7 @@ def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefi
) )
# multi-gpu evaluate # multi-gpu evaluate
if args.n_gpu > 1: if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model) model = torch.nn.DataParallel(model)
# Eval! # Eval!

2
examples/run_multiple_choice.py
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@@ -256,7 +256,7 @@ def evaluate(args, model, tokenizer, prefix="", test=False):
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# multi-gpu evaluate # multi-gpu evaluate
if args.n_gpu > 1: if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model) model = torch.nn.DataParallel(model)
# Eval! # Eval!

2
examples/run_xnli.py
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@@ -266,7 +266,7 @@ def evaluate(args, model, tokenizer, prefix=""):
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# multi-gpu eval # multi-gpu eval
if args.n_gpu > 1: if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model) model = torch.nn.DataParallel(model)
# Eval! # Eval!