[examples] consolidate summarization examples (#4837)

examples/summarization/bart/README.md → examples/summarization/README.md

-### Get Preprocessed CNN Data
+### Get CNN Data
+Both types of models do require CNN data and follow different procedures of obtaining so.
+
+#### For BART models
 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
@@ -9,22 +12,40 @@ tar -xzvf cnn_dm.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.
 
+#### For T5 models
+First, you need to download the CNN data. It's about ~400 MB and can be downloaded by
+running
+
+```bash
+python download_cnn_daily_mail.py cnn_articles_input_data.txt cnn_articles_reference_summaries.txt
+```
+
+You should confirm that each file has 11490 lines:
+
+```bash
+wc -l cnn_articles_input_data.txt # should print 11490
+wc -l cnn_articles_reference_summaries.txt # should print 11490
+```
+
 ### Evaluation
+
 To create summaries for each article in dataset, run:
 ```bash
-python evaluate_cnn.py <path_to_test.source> cnn_test_summaries.txt
+python evaluate_cnn.py <path_to_test.source> test_generations.txt <model-name>
 ```
-the default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
-
+The default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
 
 ### Training
-Run/modify `run_train.sh`
-    
-### Where is the code?
-The core model is in `src/transformers/modeling_bart.py`. This directory only contains examples.
+Run/modify `finetune_bart.sh` or `finetune_t5.sh`
 
 ## (WIP) Rouge Scores
 
+To create summaries for each article in dataset and also calculate rouge scores run:
+```bash
+python evaluate_cnn.py <path_to_test.source> test_generations.txt <model-name> --reference_path <path_to_correct_summaries> --score_path <path_to_save_rouge_scores>
+```
+The rouge scores "rouge1, rouge2, rougeL" are automatically created and saved in ``<path_to_save_rouge_scores>``.
+
 ### Stanford CoreNLP Setup
 ```
 ptb_tokenize () {

examples/summarization/bart/evaluate_cnn.py

-import argparse
-from pathlib import Path
-
-import torch
-from tqdm import tqdm
-
-from transformers import BartForConditionalGeneration, 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(
-    examples: list, out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE
-):
-    fout = Path(out_file).open("w")
-    model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
-    tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
-
-    max_length = 140
-    min_length = 55
-
-    for batch in tqdm(list(chunks(examples, 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=max_length + 2,  # +2 from original because we start at step=1 and stop before max_length
-            min_length=min_length + 1,  # +1 from original because we start at step=1
-            no_repeat_ngram_size=3,
-            early_stopping=True,
-            decoder_start_token_id=model.config.eos_token_id,
-        )
-        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(
-        "model_name", type=str, default="facebook/bart-large-cnn", help="like bart-large-cnn",
-    )
-    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()
-    examples = [" " + x.rstrip() for x in open(args.source_path).readlines()]
-    generate_summaries(examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device)
-
-
-if __name__ == "__main__":
-    run_generate()

examples/summarization/t5/download_cnn_daily_mail.py → examples/summarization/download_cnn_daily_mail.py

+# -*- coding: utf-8 -*-
 import argparse
 from pathlib import Path
 
@@ -8,8 +9,8 @@ def main(input_path, reference_path, data_dir):
     cnn_ds = tfds.load("cnn_dailymail", split="test", shuffle_files=False, data_dir=data_dir)
     cnn_ds_iter = tfds.as_numpy(cnn_ds)
 
-    test_articles_file = Path(input_path).open("w")
-    test_summaries_file = Path(reference_path).open("w")
+    test_articles_file = Path(input_path).open("w", encoding="utf-8")
+    test_summaries_file = Path(reference_path).open("w", encoding="utf-8")
 
     for example in cnn_ds_iter:
         test_articles_file.write(example["article"].decode("utf-8") + "\n")

examples/summarization/t5/evaluate_cnn.py → examples/summarization/evaluate_cnn.py

@@ -5,7 +5,10 @@ import torch
 from rouge_score import rouge_scorer, scoring
 from tqdm import tqdm
 
-from transformers import T5ForConditionalGeneration, T5Tokenizer
+from transformers import AutoModelWithLMHead, AutoTokenizer
+
+
+DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
 
 def chunks(lst, n):
@@ -14,32 +17,31 @@ def chunks(lst, n):
         yield lst[i : i + n]
 
 
-def generate_summaries(lns, output_file_path, model_size, batch_size, device):
-    output_file = Path(output_file_path).open("w")
+def generate_summaries(
+    examples: list, out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE
+):
+    fout = Path(out_file).open("w", encoding="utf-8")
+    model = AutoModelWithLMHead.from_pretrained(model_name).to(device)
 
-    model = T5ForConditionalGeneration.from_pretrained(model_size)
-    model.to(device)
-
-    tokenizer = T5Tokenizer.from_pretrained(model_size)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
 
     # update config with summarization specific params
     task_specific_params = model.config.task_specific_params
     if task_specific_params is not None:
         model.config.update(task_specific_params.get("summarization", {}))
 
-    for batch in tqdm(list(chunks(lns, batch_size))):
+    for batch in tqdm(list(chunks(examples, batch_size))):
+        if "t5" in model_name:
             batch = [model.config.prefix + text for text in batch]
+        dct = tokenizer.batch_encode_plus(batch, max_length=1024, return_tensors="pt", pad_to_max_length=True).to(
+            device
+        )
+        summaries = model.generate(**dct)
 
-        dct = tokenizer.batch_encode_plus(batch, max_length=512, return_tensors="pt", pad_to_max_length=True)
-        input_ids = dct["input_ids"].to(device)
-        attention_mask = dct["attention_mask"].to(device)
-
-        summaries = model.generate(input_ids=input_ids, attention_mask=attention_mask)
-        dec = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries]
-
+        dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
         for hypothesis in dec:
-            output_file.write(hypothesis + "\n")
-            output_file.flush()
+            fout.write(hypothesis + "\n")
+            fout.flush()
 
 
 def calculate_rouge(output_lns, reference_lns, score_path):
@@ -62,35 +64,32 @@ def calculate_rouge(output_lns, reference_lns, score_path):
 def run_generate():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "model_size",
-        type=str,
-        help="T5 model size, either 't5-small', 't5-base', 't5-large', 't5-3b', 't5-11b'. Defaults to 't5-base'.",
-        default="t5-base",
+        "input_path", type=str, help="like cnn_dm/test.source or cnn_dm/test_articles_input.txt",
     )
     parser.add_argument(
-        "input_path", type=str, help="like cnn_dm/test_articles_input.txt",
+        "output_path", type=str, help="where to save summaries",
     )
     parser.add_argument(
-        "output_path", type=str, help="where to save summaries",
+        "model_name",
+        type=str,
+        default="facebook/bart-large-cnn",
+        help="like bart-large-cnn,'t5-small', 't5-base', 't5-large', 't5-3b', 't5-11b",
     )
-    parser.add_argument("reference_path", type=str, help="like cnn_dm/test_reference_summaries.txt")
+    parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test_reference_summaries.txt")
     parser.add_argument(
-        "score_path", type=str, help="where to save the rouge score",
+        "--score_path", type=str, required=False, help="where to save the rouge score",
     )
     parser.add_argument(
-        "--batch_size", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
+        "--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.",
     )
     parser.add_argument(
-        "--no_cuda", default=False, type=bool, help="Whether to force the execution on CPU.",
+        "--bs", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
     )
-
     args = parser.parse_args()
-    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-
-    source_lns = [x.rstrip() for x in open(args.input_path).readlines()]
-
-    generate_summaries(source_lns, args.output_path, args.model_size, args.batch_size, args.device)
+    examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
 
+    generate_summaries(examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device)
+    if args.score_path is not None:
         output_lns = [x.rstrip() for x in open(args.output_path).readlines()]
         reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()]
 

examples/summarization/finetune_t5.sh

+export OUTPUT_DIR_NAME=t5
+export CURRENT_DIR=${PWD}
+export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
+
+# Make output directory if it doesn't exist
+mkdir -p $OUTPUT_DIR
+
+# Add parent directory to python path to access lightning_base.py
+export PYTHONPATH="../../":"${PYTHONPATH}"
+
+python finetune.py \
+--data_dir=./cnn-dailymail/cnn_dm \
+--model_name_or_path=t5-large \
+--model_type=t5
+--learning_rate=3e-5 \
+--train_batch_size=4 \
+--eval_batch_size=4 \
+--output_dir=$OUTPUT_DIR \
+--do_train  $@

examples/summarization/t5/README.md

-***This script evaluates the the multitask pre-trained checkpoint for ``t5-base`` (see paper [here](https://arxiv.org/pdf/1910.10683.pdf)) on the CNN/Daily Mail test dataset. Please note that the results in the paper were attained using a model fine-tuned on summarization, so that results will be worse here by approx. 0.5 ROUGE points***
-
-### Get the CNN Data
-First, you need to download the CNN data. It's about ~400 MB and can be downloaded by 
-running 
-
-```bash
-python download_cnn_daily_mail.py cnn_articles_input_data.txt cnn_articles_reference_summaries.txt
-```
-
-You should confirm that each file has 11490 lines:
-
-```bash
-wc -l cnn_articles_input_data.txt # should print 11490
-wc -l cnn_articles_reference_summaries.txt # should print 11490
-```
-
-### Generating Summaries
-
-To create summaries for each article in dataset, run:
-```bash
-python evaluate_cnn.py cnn_articles_input_data.txt cnn_generated_articles_summaries.txt cnn_articles_reference_summaries.txt rouge_score.txt
-```
-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`

examples/summarization/t5/test_t5_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
-
-
-output_file_name = "output_t5_sum.txt"
-score_file_name = "score_t5_sum.txt"
-
-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 TestT5Examples(unittest.TestCase):
-    def test_t5_cli(self):
-        stream_handler = logging.StreamHandler(sys.stdout)
-        logger.addHandler(stream_handler)
-        tmp = Path(tempfile.gettempdir()) / "utest_generations_t5_sum.hypo"
-        with tmp.open("w") as f:
-            f.write("\n".join(articles))
-
-        output_file_name = Path(tempfile.gettempdir()) / "utest_output_t5_sum.hypo"
-        score_file_name = Path(tempfile.gettempdir()) / "utest_score_t5_sum.hypo"
-
-        testargs = [
-            "evaluate_cnn.py",
-            "patrickvonplaten/t5-tiny-random",
-            str(tmp),
-            str(output_file_name),
-            str(tmp),
-            str(score_file_name),
-        ]
-
-        with patch.object(sys, "argv", testargs):
-            run_generate()
-            self.assertTrue(Path(output_file_name).exists())
-            self.assertTrue(Path(score_file_name).exists())

examples/summarization/bart/test_bart_examples.py → examples/summarization/test_summarization_examples.py

@@ -146,3 +146,34 @@ class TestBartExamples(unittest.TestCase):
             # show that targets were truncated
             self.assertEqual(batch["target_ids"].shape[1], trunc_target)  # Truncated
             self.assertGreater(max_len_target, trunc_target)  # Truncated
+
+
+class TestT5Examples(unittest.TestCase):
+    def test_t5_cli(self):
+        output_file_name = "output_t5_sum.txt"
+        score_file_name = "score_t5_sum.txt"
+        articles = ["New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+        tmp = Path(tempfile.gettempdir()) / "utest_generations_t5_sum.hypo"
+        with tmp.open("w", encoding="utf-8") as f:
+            f.write("\n".join(articles))
+
+        output_file_name = Path(tempfile.gettempdir()) / "utest_output_t5_sum.hypo"
+        score_file_name = Path(tempfile.gettempdir()) / "utest_score_t5_sum.hypo"
+
+        testargs = [
+            "evaluate_cnn.py",
+            str(tmp),
+            str(output_file_name),
+            "patrickvonplaten/t5-tiny-random",
+            "--reference_path",
+            str(tmp),
+            "--score_path",
+            str(score_file_name),
+        ]
+
+        with patch.object(sys, "argv", testargs):
+            run_generate()
+            self.assertTrue(Path(output_file_name).exists())
+            self.assertTrue(Path(score_file_name).exists())