Add instructions for paraphrasing model (#1968)

Summary: Pull Request resolved: #1968

Reviewed By: ngoyal2707

Differential Revision: D20860682

Pulled By: myleott

fbshipit-source-id: b7dced493410a4b9e217e4735eb9cdd0370ad47e

Author: Myle Ott

examples/paraphraser/README.md

@@ -0,0 +1,46 @@
+# Paraphrasing with round-trip translation and mixture of experts
+
+Machine translation models can be used to paraphrase text by translating it to
+an intermediate language and back (round-trip translation).
+
+This example shows how to paraphrase text by first passing it to an
+English-French translation model, followed by a French-English [mixture of
+experts translation model](/examples/translation_moe).
+
+##### 0. Setup
+
+Clone fairseq from source and install necessary dependencies:
+```bash
+git clone https://github.com/pytorch/fairseq.git
+cd fairseq
+pip install --editable .
+pip install sacremoses sentencepiece
+```
+
+##### 1. Download models
+```bash
+wget https://dl.fbaipublicfiles.com/fairseq/models/paraphraser.en-fr.tar.gz
+wget https://dl.fbaipublicfiles.com/fairseq/models/paraphraser.fr-en.hMoEup.tar.gz
+tar -xzvf paraphraser.en-fr.tar.gz
+tar -xzvf paraphraser.fr-en.hMoEup.tar.gz
+```
+
+##### 2. Paraphrase
+```bash
+python examples/paraphraser/paraphrase.py \
+    --en2fr paraphraser.en-fr \
+    --fr2en paraphraser.fr-en.hMoEup
+# Example input:
+#   The new date for the Games, postponed for a year in response to the coronavirus pandemic, gives athletes time to recalibrate their training schedules.
+# Example outputs:
+#   Delayed one year in response to the coronavirus pandemic, the new date of the Games gives athletes time to rebalance their training schedule.
+#   The new date of the Games, which was rescheduled one year in response to the coronavirus (CV) pandemic, gives athletes time to rebalance their training schedule.
+#   The new date of the Games, postponed one year in response to the coronavirus pandemic, provides athletes with time to rebalance their training schedule.
+#   The Games' new date, postponed one year in response to the coronavirus pandemic, gives athletes time to rebalance their training schedule.
+#   The new Games date, postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their training schedule.
+#   The new date of the Games, which was postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their training schedule.
+#   The new date of the Games, postponed one year in response to the coronavirus pandemic, gives athletes time to rebalance their training schedule.
+#   The new date of the Games, postponed one year in response to the coronavirus pandemic, gives athletes time to re-balance their training schedule.
+#   The new date of the Games, postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their schedule of training.
+#   The new date of the Games, postponed one year in response to the pandemic of coronavirus, gives the athletes time to rebalance their training schedule.
+```

examples/paraphraser/paraphrase.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python3 -u
+
+import argparse
+import fileinput
+import logging
+import os
+import sys
+
+from fairseq.models.transformer import TransformerModel
+
+
+logging.getLogger().setLevel(logging.INFO)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='')
+    parser.add_argument('--en2fr', required=True,
+                        help='path to en2fr model')
+    parser.add_argument('--fr2en', required=True,
+                        help='path to fr2en mixture of experts model')
+    parser.add_argument('--user-dir',
+                        help='path to fairseq examples/translation_moe/src directory')
+    parser.add_argument('--num-experts', type=int, default=10,
+                        help='(keep at 10 unless using a different model)')
+    parser.add_argument('files', nargs='*', default=['-'],
+                        help='input files to paraphrase; "-" for stdin')
+    args = parser.parse_args()
+
+    if args.user_dir is None:
+        args.user_dir = os.path.join(
+            os.path.dirname(os.path.dirname(os.path.abspath(__file__))),  # examples/
+            'translation_moe',
+            'src',
+        )
+        if os.path.exists(args.user_dir):
+            logging.info('found user_dir:' + args.user_dir)
+        else:
+            raise RuntimeError(
+                'cannot find fairseq examples/translation_moe/src '
+                '(tried looking here: {})'.format(args.user_dir)
+            )
+
+    logging.info('loading en2fr model from:' + args.en2fr)
+    en2fr = TransformerModel.from_pretrained(
+        model_name_or_path=args.en2fr,
+        tokenizer='moses',
+        bpe='sentencepiece',
+    ).eval()
+
+    logging.info('loading fr2en model from:' + args.fr2en)
+    fr2en = TransformerModel.from_pretrained(
+        model_name_or_path=args.fr2en,
+        tokenizer='moses',
+        bpe='sentencepiece',
+        user_dir=args.user_dir,
+        task='translation_moe',
+    ).eval()
+
+    def gen_paraphrases(en):
+        fr = en2fr.translate(en)
+        return [
+            fr2en.translate(fr, inference_step_args={'expert': i})
+            for i in range(args.num_experts)
+        ]
+
+    logging.info('Type the input sentence and press return:')
+    for line in fileinput.input(args.files):
+        line = line.strip()
+        if len(line) == 0:
+            continue
+        for paraphrase in gen_paraphrases(line):
+            print(paraphrase)
+
+
+if __name__ == '__main__':
+    main()

fairseq/hub_utils.py

@@ -145,6 +145,7 @@ def generate(
         beam: int = 5,
         verbose: bool = False,
         skip_invalid_size_inputs=False,
+        inference_step_args=None,
         **kwargs
     ) -> List[List[Dict[str, torch.Tensor]]]:
         if torch.is_tensor(tokenized_sentences) and tokenized_sentences.dim() == 1:
@@ -159,10 +160,13 @@ def generate(
             setattr(gen_args, k, v)
         generator = self.task.build_generator(self.models, gen_args)
 
+        inference_step_args = inference_step_args or {}
         results = []
         for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs):
             batch = utils.apply_to_sample(lambda t: t.to(self.device), batch)
-            translations = self.task.inference_step(generator, self.models, batch)
+            translations = self.task.inference_step(
+                generator, self.models, batch, **inference_step_args
+            )
             for id, hypos in zip(batch["id"].tolist(), translations):
                 results.append((id, hypos))