1) replaced fstring 2) fixed error from max-positions arg

Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/787

Differential Revision: D16562052

fbshipit-source-id: 640e30b2378ec917d60092558d3088a77f9741cb

fairseq/tasks/sentence_prediction.py

@@ -41,8 +41,6 @@ class SentencePredictionTask(FairseqTask):
         """Add task-specific arguments to the parser."""
         parser.add_argument('data', metavar='FILE',
                             help='file prefix for data')
-        parser.add_argument('--max-positions', type=int, default=512,
-                            help='max input length')
         parser.add_argument('--num-classes', type=int, default=-1,
                             help='number of classes')
         parser.add_argument('--init-token', type=int, default=None,
@@ -160,7 +158,7 @@ class SentencePredictionTask(FairseqTask):
                     )
                 )
         else:
-            label_path = f"{get_path('label', split)}.label"
+            label_path = "{0}.label".format(get_path('label', split))
             if os.path.exists(label_path):
                 dataset.update(
                     target=RawLabelDataset([
@@ -182,7 +180,7 @@ class SentencePredictionTask(FairseqTask):
                 sort_order=[shuffle],
             )
 
-        print(f"| Loaded {split} with #samples: {len(dataset)}")
+        print("| Loaded {0} with #samples: {1}".format(split, len(dataset)))
 
         self.datasets[split] = dataset
         return self.datasets[split]

train.py

@@ -9,7 +9,6 @@ Train a new model on one or across multiple GPUs.
 
 import collections
 import math
-import os
 import random
 
 import torch
@@ -258,7 +257,7 @@ def get_valid_stats(trainer, args, extra_meters=None):
     stats['ppl'] = utils.get_perplexity(nll_loss.avg)
     stats['num_updates'] = trainer.get_num_updates()
     if hasattr(checkpoint_utils.save_checkpoint, 'best'):
-        key = f'best_{args.best_checkpoint_metric}'
+        key = 'best_{0}'.format(args.best_checkpoint_metric)
         best_function = max if args.maximize_best_checkpoint_metric else min
 
         current_metric = None