improve device usage

examples/summarization/README.md

@@ -29,7 +29,7 @@ And move all the stories to the same folder. We will refer as `$DATA_PATH` the p
 python run_summarization.py \
     --documents_dir $DATA_PATH \
     --summaries_output_dir $SUMMARIES_PATH \ # optional
-    --visible_gpus 0,1,2 \
+    --to_cpu false \
     --batch_size 4 \
     --min_length 50 \
     --max_length 200 \
@@ -39,7 +39,7 @@ python run_summarization.py \
     --compute_rouge true
 ```
 
-The ROUGE scores will be displayed in the console at the end of evaluation and written in a `rouge_scores.txt` file.
+The scripts executes on GPU if one is available and if `to_cpu` is not set to `true`. Inference on multiple GPUs is not suported yet. The ROUGE scores will be displayed in the console at the end of evaluation and written in a `rouge_scores.txt` file. The script takes 30 hours to compute with a single Tesla V100 GPU and a batch size of 10 (300,000 texts to summarize).
 
 ## Summarize any text
 
@@ -49,7 +49,7 @@ Put the documents that you would like to summarize in a folder (the path to whic
 python run_summarization.py \
     --documents_dir $DATA_PATH \
     --summaries_output_dir $SUMMARIES_PATH \ # optional
-    --visible_gpus 0,1,2 \
+    --to_cpu false \
     --batch_size 4 \
     --min_length 50 \
     --max_length 200 \
@@ -58,4 +58,4 @@ python run_summarization.py \
     --block_trigram true \
 ```
 
-If you want to compute ROUGE on another dataset you will need to tweak the stories/summaries import in `utils_summarization.py`
+You may want to play around with `min_length`, `max_length` and `alpha` to suit your use case. If you want to compute ROUGE on another dataset you will need to tweak the stories/summaries import in `utils_summarization.py` and tell it where to fetch the reference summaries.

examples/summarization/convert_bertabs_original_pytorch_checkpoint.py

@@ -12,10 +12,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" Convert BertExtAbs's checkpoints
+""" Convert BertExtAbs's checkpoints.
 
-The file currently does not do much as we ended up copying the exact model
-structure, but I leave it here in case we ever want to refactor the model.
+The script looks like it is doing something trivial but it is not. The "weights"
+proposed by the authors are actually the entire model pickled. We need to load
+the model within the original codebase to be able to only save its `state_dict`.
 """
 
 import argparse

examples/summarization/modeling_bertabs.py

@@ -847,14 +847,12 @@ class Translator(object):
        global_scores (:obj:`GlobalScorer`):
          object to rescore final translations
        copy_attn (bool): use copy attention during translation
-       cuda (bool): use cuda
        beam_trace (bool): trace beam search for debugging
        logger(logging.Logger): logger.
     """
 
     def __init__(self, args, model, vocab, symbols, global_scorer=None, logger=None):
         self.logger = logger
-        self.cuda = args.visible_gpus != "-1"
 
         self.args = args
         self.model = model

examples/summarization/run_summarization.py

@@ -185,7 +185,7 @@ def save_summaries(summaries, path, original_document_name):
 def build_data_iterator(args, tokenizer):
     dataset = load_and_cache_examples(args, tokenizer)
     sampler = SequentialSampler(dataset)
-    collate_fn = lambda data: collate(data, tokenizer, block_size=512)
+    collate_fn = lambda data: collate(data, tokenizer, block_size=512, device=args.device)
     iterator = DataLoader(
         dataset, sampler=sampler, batch_size=args.batch_size, collate_fn=collate_fn,
     )
@@ -198,7 +198,7 @@ def load_and_cache_examples(args, tokenizer):
     return dataset
 
 
-def collate(data, tokenizer, block_size):
+def collate(data, tokenizer, block_size, device):
     """ Collate formats the data passed to the data loader.
 
     In particular we tokenize the data batch after batch to avoid keeping them
@@ -224,9 +224,9 @@ def collate(data, tokenizer, block_size):
     batch = Batch(
         document_names=names,
         batch_size=len(encoded_stories),
-        src=encoded_stories,
-        segs=encoder_token_type_ids,
-        mask_src=encoder_mask,
+        src=encoded_stories.to(device),
+        segs=encoder_token_type_ids.to(device),
+        mask_src=encoder_mask.to(device),
         tgt_str=summaries,
     )
 
@@ -271,10 +271,10 @@ def main():
     )
     # EVALUATION options
     parser.add_argument(
-        "--visible_gpus",
-        default=-1,
-        type=int,
-        help="Number of GPUs with which to do the training.",
+        "--to_cpu",
+        default=False,
+        type=bool,
+        help="Whether to force the execution on CPU.",
     )
     parser.add_argument(
         "--batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.",
@@ -311,8 +311,11 @@ def main():
         help="Whether to block the existence of repeating trigrams in the text generated by beam search.",
     )
     args = parser.parse_args()
-    args.device = torch.device("cpu") if args.visible_gpus == -1 else torch.device("cuda")
 
+    # Select device (distibuted not available)
+    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.to_cpu else "cpu")
+
+    # Check the existence of directories
     if not args.summaries_output_dir:
         args.summaries_output_dir = args.documents_dir