default arg fix (#2937)

examples/ner/transformer_base.py

@@ -248,16 +248,22 @@ def generic_train(model, args):
         filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=5
     )
 
-    trainer = pl.Trainer(
+    train_params = dict(
         accumulate_grad_batches=args.gradient_accumulation_steps,
         gpus=args.n_gpu,
         max_epochs=args.num_train_epochs,
-        use_amp=args.fp16,
-        amp_level=args.fp16_opt_level,
-        distributed_backend="ddp",
         gradient_clip_val=args.max_grad_norm,
         checkpoint_callback=checkpoint_callback,
     )
+    if args.fp16:
+        train_params["use_amp"] = args.fp16
+        train_params["amp_level"] = args.fp16_opt_level
+
+    if args.n_gpu > 1:
+        train_params["distributed_backend"] = "ddp"
+
+    trainer = pl.Trainer(**train_params)
+
     if args.do_train:
         trainer.fit(model)