[examples] summarization/bart/finetune.py supports t5 (#3824)

renames `run_bart_sum.py` to `finetune.py`

examples/summarization/bart/run_bart_sum.py → examples/summarization/bart/finetune.py

@@ -19,7 +19,7 @@ except ImportError:
 logger = logging.getLogger(__name__)
 
 
-class BartSystem(BaseTransformer):
+class SummarizationTrainer(BaseTransformer):
 
     mode = "language-modeling"
 
@@ -64,18 +64,18 @@ class BartSystem(BaseTransformer):
         return {"avg_val_loss": avg_loss, "log": tensorboard_logs}
 
     def test_step(self, batch, batch_idx):
-        # NOTE: this generation will not use the cache.
         pad_token_id = self.tokenizer.pad_token_id
         source_ids, source_mask, y = SummarizationDataset.trim_seq2seq_batch(batch, pad_token_id)
-        # NOTE: these kwargs get more speed and lower quality summaries than those in evaluate_cnn.py.
+        # NOTE: the following kwargs get more speed and lower quality summaries than those in evaluate_cnn.py
         generated_ids = self.model.generate(
-            source_ids,
-            source_mask,
+            input_ids=source_ids,
+            attention_mask=source_mask,
             num_beams=1,
             max_length=80,
             repetition_penalty=2.5,
             length_penalty=1.0,
             early_stopping=True,
+            use_cache=True,
         )
         preds = [
             self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True)
@@ -161,20 +161,20 @@ def main(args):
     if not args.output_dir:
         args.output_dir = os.path.join("./results", f"{args.task}_{args.model_type}_{time.strftime('%Y%m%d_%H%M%S')}",)
         os.makedirs(args.output_dir)
-    model = BartSystem(args)
+    model = SummarizationTrainer(args)
     trainer = generic_train(model, args)
 
     # Optionally, predict on dev set and write to output_dir
     if args.do_predict:
         checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpointepoch=*.ckpt"), recursive=True)))
-        BartSystem.load_from_checkpoint(checkpoints[-1])
+        SummarizationTrainer.load_from_checkpoint(checkpoints[-1])
         trainer.test(model)
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     add_generic_args(parser, os.getcwd())
-    parser = BartSystem.add_model_specific_args(parser, os.getcwd())
+    parser = SummarizationTrainer.add_model_specific_args(parser, os.getcwd())
     args = parser.parse_args()
 
     main(args)

examples/summarization/bart/run_train.sh

@@ -8,7 +8,7 @@ mkdir -p $OUTPUT_DIR
 # Add parent directory to python path to access transformer_base.py
 export PYTHONPATH="../../":"${PYTHONPATH}"
 
-python run_bart_sum.py \
+python finetune.py \
 --data_dir=./cnn-dailymail/cnn_dm \
 --model_type=bart \
 --model_name_or_path=bart-large \

examples/summarization/bart/run_train_tiny.sh

@@ -14,7 +14,7 @@ mkdir -p $OUTPUT_DIR
 
 # Add parent directory to python path to access transformer_base.py and utils.py
 export PYTHONPATH="../../":"${PYTHONPATH}"
-python run_bart_sum.py \
+python finetune.py \
 --data_dir=cnn_tiny/ \
 --model_type=bart \
 --model_name_or_path=sshleifer/bart-tiny-random \

examples/summarization/bart/test_bart_examples.py

@@ -12,7 +12,7 @@ from torch.utils.data import DataLoader
 from transformers import BartTokenizer
 
 from .evaluate_cnn import run_generate
-from .run_bart_sum import main
+from .finetune import main
 from .utils import SummarizationDataset
 
 
@@ -92,9 +92,27 @@ class TestBartExamples(unittest.TestCase):
         args_d.update(
             data_dir=tmp_dir, model_type="bart", train_batch_size=2, eval_batch_size=2, n_gpu=0, output_dir=output_dir,
         )
+        main(argparse.Namespace(**args_d))
+        args_d.update({"do_train": False, "do_predict": True})
+        main(argparse.Namespace(**args_d))
 
-        args = argparse.Namespace(**args_d)
-        main(args)
+    def test_t5_run_sum_cli(self):
+        args_d: dict = DEFAULT_ARGS.copy()
+        tmp_dir = make_test_data_dir()
+        output_dir = tempfile.mkdtemp(prefix="output_")
+        args_d.update(
+            data_dir=tmp_dir,
+            model_type="t5",
+            model_name_or_path="patrickvonplaten/t5-tiny-random",
+            train_batch_size=2,
+            eval_batch_size=2,
+            n_gpu=0,
+            output_dir=output_dir,
+            do_predict=True,
+        )
+        main(argparse.Namespace(**args_d))
+        # args_d.update({"do_train": False, "do_predict": True})
+        # main(argparse.Namespace(**args_d))
 
     def test_bart_summarization_dataset(self):
         tmp_dir = Path(tempfile.gettempdir())

examples/summarization/t5/README.md

@@ -15,7 +15,7 @@ wc -l cnn_articles_input_data.txt # should print 11490
 wc -l cnn_articles_reference_summaries.txt # should print 11490
 ```
 
-### Usage
+### Generating Summaries
 
 To create summaries for each article in dataset, run:
 ```bash
@@ -23,3 +23,7 @@ python evaluate_cnn.py cnn_articles_input_data.txt cnn_generated_articles_summar
 ```
 The default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
 The rouge scores "rouge1, rouge2, rougeL" are automatically created and saved in ``rouge_score.txt``.
+
+
+### Finetuning
+Pass model_type=t5 and model `examples/summarization/bart/finetune.py`