Summarization Examples: add Bart CNN Evaluation (#3082)

* Rename and improve example

* Add test

* slightly faster test

* style

* This breaks remy prolly

* shorter test string

* no slow

* newdir structure

* New tree

* Style

* shorter

* docs

* clean

* Attempt future import

* more import hax

examples/summarization/bart/README.md

+### Get the CNN/Daily Mail Data
+To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:
+
+```bash
+tar -xvf cnn_stories.tgz && tar -xvf dailymail_stories.tgz
+```
+this should make a directory called cnn_dm/ with files like `test.source`. 
+To use your own data, copy that files format. Each article to be summarized is on its own line.
+
+### Usage
+To create summaries for each article in dataset, run:
+```bash
+python evaluate_cnn.py <path_to_test.source> cnn_test_summaries.txt
+```
+the default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
+
+### Where is the code?
+The core model is in `src/transformers/modeling_bart.py`. This directory only contains examples.
+
+### (WIP) Rouge Scores
+
+### Stanford CoreNLP Setup
+```
+ptb_tokenize () {
+    cat $1 | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > $2
+}
+
+sudo apt install openjdk-8-jre-headless
+sudo apt-get install ant
+wget http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
+unzip stanford-corenlp-full-2018-10-05.zip
+cd stanford-corenlp-full-2018-10-05
+export CLASSPATH=stanford-corenlp-3.9.2.jar:stanford-corenlp-3.9.2-models.jar
+```
+### Rouge Setup
+Install `files2rouge` following the instructions at [here](https://github.com/pltrdy/files2rouge).
+I also needed to run `sudo apt-get install libxml-parser-perl`
+
+```python
+from files2rouge import files2rouge
+from files2rouge import settings
+files2rouge.run(<path_to_tokenized_hypo>,
+                <path_to_tokenized_target>,
+               saveto='rouge_output.txt')
+```

examples/summarization/bart/evaluate_cnn.py

+import argparse
+from pathlib import Path
+
+import torch
+from tqdm import tqdm
+
+from transformers import BartForMaskedLM, BartTokenizer
+
+
+DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def chunks(lst, n):
+    """Yield successive n-sized chunks from lst."""
+    for i in range(0, len(lst), n):
+        yield lst[i : i + n]
+
+
+def generate_summaries(lns, out_file, batch_size=8, device=DEFAULT_DEVICE):
+    fout = Path(out_file).open("w")
+    model = BartForMaskedLM.from_pretrained("bart-large-cnn", output_past=True,)
+    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)
+        summaries = model.generate(
+            input_ids=dct["input_ids"].to(device),
+            attention_mask=dct["attention_mask"].to(device),
+            num_beams=4,
+            length_penalty=2.0,
+            max_length=140,
+            min_len=55,
+            no_repeat_ngram_size=3,
+        )
+        dec = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries]
+        for hypothesis in dec:
+            fout.write(hypothesis + "\n")
+            fout.flush()
+
+
+def _run_generate():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "source_path", type=str, help="like cnn_dm/test.source",
+    )
+    parser.add_argument(
+        "output_path", type=str, help="where to save summaries",
+    )
+    parser.add_argument(
+        "--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.",
+    )
+    parser.add_argument(
+        "--bs", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
+    )
+    args = parser.parse_args()
+    lns = [" " + x.rstrip() for x in open(args.source_path).readlines()]
+    generate_summaries(lns, args.output_path, batch_size=args.bs, device=args.device)
+
+
+if __name__ == "__main__":
+    _run_generate()

examples/summarization/bart/test_bart_examples.py

+import logging
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+from .evaluate_cnn import _run_generate
+
+
+articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
+
+logging.basicConfig(level=logging.DEBUG)
+
+logger = logging.getLogger()
+
+
+class TestBartExamples(unittest.TestCase):
+    def test_bart_cnn_cli(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+        tmp = Path(tempfile.gettempdir()) / "utest_generations.hypo"
+        with tmp.open("w") as f:
+            f.write("\n".join(articles))
+        testargs = ["evaluate_cnn.py", str(tmp), "output.txt"]
+        with patch.object(sys, "argv", testargs):
+            _run_generate()
+            self.assertTrue(Path("output.txt").exists())

examples/summarization/README.md → examples/summarization/bertabs/README.md

@@ -15,7 +15,7 @@ pip install nltk py-rouge
 cd examples/summarization
 ```
 
-## Reproduce the authors' results on ROUGE
+## Reproduce the authors'  ROUGE score
 
 To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:
 

examples/summarization/run_summarization.py → examples/summarization/bertabs/run_summarization.py

@@ -11,12 +11,13 @@ from tqdm import tqdm
 
 from modeling_bertabs import BertAbs, build_predictor
 from transformers import BertTokenizer
-from utils_summarization import (
-    SummarizationDataset,
+
+from .utils_summarization import (
+    CNNDMDataset,
     build_mask,
     compute_token_type_ids,
     encode_for_summarization,
-    fit_to_block_size,
+    truncate_or_pad,
 )
 
 
@@ -194,7 +195,7 @@ def build_data_iterator(args, tokenizer):
 
 
 def load_and_cache_examples(args, tokenizer):
-    dataset = SummarizationDataset(args.documents_dir)
+    dataset = CNNDMDataset(args.documents_dir)
     return dataset
 
 
@@ -211,7 +212,7 @@ def collate(data, tokenizer, block_size, device):
 
     encoded_text = [encode_for_summarization(story, summary, tokenizer) for _, story, summary in data]
     encoded_stories = torch.tensor(
-        [fit_to_block_size(story, block_size, tokenizer.pad_token_id) for story, _ in encoded_text]
+        [truncate_or_pad(story, block_size, tokenizer.pad_token_id) for story, _ in encoded_text]
     )
     encoder_token_type_ids = compute_token_type_ids(encoded_stories, tokenizer.cls_token_id)
     encoder_mask = build_mask(encoded_stories, tokenizer.pad_token_id)

examples/summarization/test_utils_summarization.py → examples/summarization/bertabs/test_utils_summarization.py

@@ -17,7 +17,7 @@ import unittest
 import numpy as np
 import torch
 
-from utils_summarization import build_mask, compute_token_type_ids, fit_to_block_size, process_story
+from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
 
 
 class SummarizationDataProcessingTest(unittest.TestCase):
@@ -28,19 +28,19 @@ class SummarizationDataProcessingTest(unittest.TestCase):
         """ Pad the sequence with 0 if the sequence is smaller than the block size."""
         sequence = [1, 2, 3, 4]
         expected_output = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
-        self.assertEqual(fit_to_block_size(sequence, self.block_size, 0), expected_output)
+        self.assertEqual(truncate_or_pad(sequence, self.block_size, 0), expected_output)
 
     def test_fit_to_block_sequence_fit_exactly(self):
         """ Do nothing if the sequence is the right size. """
         sequence = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         expected_output = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-        self.assertEqual(fit_to_block_size(sequence, self.block_size, 0), expected_output)
+        self.assertEqual(truncate_or_pad(sequence, self.block_size, 0), expected_output)
 
     def test_fit_to_block_sequence_too_big(self):
         """ Truncate the sequence if it is too long. """
         sequence = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
         expected_output = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-        self.assertEqual(fit_to_block_size(sequence, self.block_size, 0), expected_output)
+        self.assertEqual(truncate_or_pad(sequence, self.block_size, 0), expected_output)
 
     def test_process_story_no_highlights(self):
         """ Processing a story with no highlights returns an empty list for the summary.

examples/summarization/utils_summarization.py → examples/summarization/bertabs/utils_summarization.py

@@ -10,7 +10,7 @@ from torch.utils.data import Dataset
 # ------------
 
 
-class SummarizationDataset(Dataset):
+class CNNDMDataset(Dataset):
     """ Abstracts the dataset used to train seq2seq models.
 
     The class will process the documents that are located in the specified
@@ -62,11 +62,11 @@ class SummarizationDataset(Dataset):
 def process_story(raw_story):
     """ Extract the story and summary from a story file.
 
-    Attributes:
+    Arguments:
         raw_story (str): content of the story file as an utf-8 encoded string.
 
     Raises:
-        IndexError: If the stoy is empty or contains no highlights.
+        IndexError: If the story is empty or contains no highlights.
     """
     nonempty_lines = list(filter(lambda x: len(x) != 0, [line.strip() for line in raw_story.split("\n")]))
 
@@ -107,7 +107,7 @@ def _add_missing_period(line):
 # --------------------------
 
 
-def fit_to_block_size(sequence, block_size, pad_token_id):
+def truncate_or_pad(sequence, block_size, pad_token_id):
     """ Adapt the source and target sequences' lengths to the block size.
     If the sequence is shorter we append padding token to the right of the sequence.
     """