Add wmt translation example (#3428)

* add translation example

* make style

* adapt docstring

* add gpu device as input for example

* small renaming

* better README

examples/requirements.txt

@@ -3,5 +3,6 @@ tensorboard
 scikit-learn
 seqeval
 psutil
+sacrebleu
 rouge-score
-tensorflow_datasets
+tensorflow_datasets

examples/translation/t5/README.md

@@ -0,0 +1,51 @@
+***This script evaluates the multitask pre-trained checkpoint for ``t5-base`` (see paper [here](https://arxiv.org/pdf/1910.10683.pdf)) on the English to German WMT dataset. Please note that the results in the paper were attained using a model fine-tuned on translation, so that results will be worse here by approx. 1.5 BLEU points***
+
+### Intro
+
+This example shows how T5 (here the official [paper](https://arxiv.org/abs/1910.10683)) can be
+evaluated on the WMT English-German dataset.
+
+### Get the WMT Data
+
+To be able to reproduce the authors' results on WMT English to German, you first need to download 
+the WMT14 en-de news datasets.
+Go on Stanford's official NLP [website](https://nlp.stanford.edu/projects/nmt/) and find "newstest2013.en" and "newstest2013.de" under WMT'14 English-German data or download the dataset directly via:
+
+```bash
+curl https://nlp.stanford.edu/projects/nmt/data/wmt14.en-de/newstest2013.en > newstest2013.en
+curl https://nlp.stanford.edu/projects/nmt/data/wmt14.en-de/newstest2013.de > newstest2013.de
+```
+
+You should have 3000 sentence in each file. You can verify this by running:
+
+```bash
+wc -l newstest2013.en  # should give 3000
+```
+
+### Usage
+
+Let's check the longest and shortest sentence in our file to find reasonable decoding hyperparameters: 
+
+Get the longest and shortest sentence:
+
+```bash 
+awk '{print NF}' newstest2013.en | sort -n | head -1 # shortest sentence has 1 word
+awk '{print NF}' newstest2013.en | sort -n | tail -1 # longest sentence has 106 words
+```
+
+We will set our `max_length` to ~3 times the longest sentence and leave `min_length` to its default value of 0.
+We decode with beam search `num_beams=4` as proposed in the paper. Also as is common in beam search we set `early_stopping=True` and `length_penalty=2.0`.
+
+To create translation for each in dataset and get a final BLEU score, run:
+```bash
+python evaluate_wmt.py <path_to_newstest2013.en> newstest2013_de_translations.txt <path_to_newstest2013.de> newsstest2013_en_de_bleu.txt
+```
+the default batch size, 16, 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_t5.py`. This directory only contains examples.
+
+### BLEU Scores
+
+The BLEU score is calculated using [sacrebleu](https://github.com/mjpost/sacreBLEU) by mjpost.
+To get the BLEU score we used 

examples/translation/t5/evaluate_wmt.py

@@ -0,0 +1,90 @@
+import argparse
+from pathlib import Path
+
+import torch
+from tqdm import tqdm
+
+from sacrebleu import corpus_bleu
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+
+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_translations(lns, output_file_path, batch_size, device):
+    output_file = Path(output_file_path).open("w")
+
+    model = T5ForConditionalGeneration.from_pretrained("t5-base")
+    model.to(device)
+
+    tokenizer = T5Tokenizer.from_pretrained("t5-base")
+
+    # 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("translation_en_to_de", {}))
+
+    for batch in tqdm(list(chunks(lns, batch_size))):
+        batch = [model.config.prefix + text for text in batch]
+
+        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)
+
+        translations = 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 translations]
+
+        for hypothesis in dec:
+            output_file.write(hypothesis + "\n")
+            output_file.flush()
+
+
+def calculate_bleu_score(output_lns, refs_lns, score_path):
+    bleu = corpus_bleu(output_lns, [refs_lns])
+    result = "BLEU score: {}".format(bleu.score)
+    score_file = Path(score_path).open("w")
+    score_file.write(result)
+
+
+def run_generate():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "input_path", type=str, help="like wmt/newstest2013.en",
+    )
+    parser.add_argument(
+        "output_path", type=str, help="where to save translation",
+    )
+    parser.add_argument(
+        "reference_path", type=str, help="like wmt/newstest2013.de",
+    )
+    parser.add_argument(
+        "score_path", type=str, help="where to save the bleu score",
+    )
+    parser.add_argument(
+        "--batch_size", type=int, default=16, required=False, help="batch size: how many to summarize at a time",
+    )
+    parser.add_argument(
+        "--no_cuda", default=False, type=bool, help="Whether to force the execution on CPU.",
+    )
+
+    args = parser.parse_args()
+    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+
+    dash_pattern = (" ##AT##-##AT## ", "-")
+
+    input_lns = [x.strip().replace(dash_pattern[0], dash_pattern[1]) for x in open(args.input_path).readlines()]
+
+    generate_translations(input_lns, args.output_path, args.batch_size, args.device)
+
+    output_lns = [x.strip() for x in open(args.output_path).readlines()]
+    refs_lns = [x.strip().replace(dash_pattern[0], dash_pattern[1]) for x in open(args.reference_path).readlines()]
+
+    calculate_bleu_score(output_lns, refs_lns, args.score_path)
+
+
+if __name__ == "__main__":
+    run_generate()

examples/translation/t5/test_t5_examples.py

@@ -0,0 +1,28 @@
+import logging
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+from .evaluate_wmt import run_generate
+
+
+text = [" 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.hypo"
+        with tmp.open("w") as f:
+            f.write("\n".join(text))
+        testargs = ["evaluate_cnn.py", str(tmp), "output.txt", str(tmp), "score.txt"]
+        with patch.object(sys, "argv", testargs):
+            run_generate()
+            self.assertTrue(Path("output.txt").exists())