Merge pull request #896 from zijunsun/master

fix multi-gpu training bug when using fp16

examples/run_glue.py

@@ -92,6 +92,10 @@ def train(args, train_dataset, model, tokenizer):
             raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
         model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
 
+    # multi-gpu training (should be after apex fp16 initialization)
+    if args.n_gpu > 1:
+        model = torch.nn.DataParallel(model)
+
     # Distributed training (should be after apex fp16 initialization)
     if args.local_rank != -1:
         model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
@@ -418,8 +422,6 @@ def main():
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
 
     model.to(args.device)
-    if args.n_gpu > 1:
-        model = torch.nn.DataParallel(model)
 
     logger.info("Training/evaluation parameters %s", args)
 

examples/run_squad.py

@@ -101,6 +101,10 @@ def train(args, train_dataset, model, tokenizer):
             raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
         model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
 
+    # multi-gpu training (should be after apex fp16 initialization)
+    if args.n_gpu > 1:
+        model = torch.nn.DataParallel(model)
+
     # Distributed training (should be after apex fp16 initialization)
     if args.local_rank != -1:
         model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
@@ -460,8 +464,6 @@ def main():
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
 
     model.to(args.device)
-    if args.n_gpu > 1:
-        model = torch.nn.DataParallel(model)
 
     logger.info("Training/evaluation parameters %s", args)