load args from model for eval_lm

eval_lm.py

@@ -14,23 +14,28 @@ from fairseq.meters import StopwatchMeter, TimeMeter
 from fairseq.sequence_scorer import SequenceScorer
 
 
-def main(args):
-    assert args.path is not None, '--path required for evaluation!'
+def main(parsed_args):
+    assert parsed_args.path is not None, '--path required for evaluation!'
 
-    args.tokens_per_sample = getattr(args, 'tokens_per_sample', 1024)
+    print(parsed_args)
+
+    use_cuda = torch.cuda.is_available() and not parsed_args.cpu
+
+    task = tasks.setup_task(parsed_args)
+
+    # Load ensemble
+    print('| loading model(s) from {}'.format(parsed_args.path))
+    models, args = utils.load_ensemble_for_inference(parsed_args.path.split(':'), task)
+
+    args.__dict__.update(parsed_args.__dict__)
     print(args)
 
-    use_cuda = torch.cuda.is_available() and not args.cpu
+    task.args = args
 
     # Load dataset splits
-    task = tasks.setup_task(args)
     task.load_dataset(args.gen_subset)
     print('| {} {} {} examples'.format(args.data, args.gen_subset, len(task.dataset(args.gen_subset))))
 
-    # Load ensemble
-    print('| loading model(s) from {}'.format(args.path))
-    models, _ = utils.load_ensemble_for_inference(args.path.split(':'), task)
-
     # Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer)
     for model in models:
         model.make_generation_fast_()

fairseq/models/transformer.py

@@ -193,6 +193,8 @@ class TransformerLanguageModel(FairseqLanguageModel):
         else:
             embed_tokens = Embedding(len(task.dictionary), args.decoder_embed_dim, task.dictionary.pad())
 
+        print(args)
+
         decoder = TransformerDecoder(args, task.dictionary, embed_tokens, no_encoder_attn=True)
         return TransformerLanguageModel(decoder)