Bart example: model.to(device) (#3194)

examples/summarization/bart/evaluate_cnn.py

@@ -18,7 +18,7 @@ def chunks(lst, n):
 
 def generate_summaries(lns, out_file, batch_size=8, device=DEFAULT_DEVICE):
     fout = Path(out_file).open("w")
-    model = BartForConditionalGeneration.from_pretrained("bart-large-cnn", output_past=True,)
+    model = BartForConditionalGeneration.from_pretrained("bart-large-cnn", output_past=True,).to(device)
     tokenizer = BartTokenizer.from_pretrained("bart-large")
     for batch in tqdm(list(chunks(lns, batch_size))):
         dct = tokenizer.batch_encode_plus(batch, max_length=1024, return_tensors="pt", pad_to_max_length=True)