Improve interactive generation (support --tokenizer and --bpe)

Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/734

Differential Revision: D16377044

Pulled By: myleott

fbshipit-source-id: 37d5553d76aa7c653113fec089f59710281c31d7

docs/getting_started.rst

@@ -19,23 +19,27 @@ flag to :ref:`fairseq-generate`. Prior to BPE, input text needs to be tokenized
 using ``tokenizer.perl`` from
 `mosesdecoder <https://github.com/moses-smt/mosesdecoder>`__.
 
-Let's use :ref:`fairseq-interactive` to generate translations
-interactively. Here, we use a beam size of 5:
+Let's use :ref:`fairseq-interactive` to generate translations interactively.
+Here, we use a beam size of 5 and preprocess the input with the Moses
+tokenizer and the given Byte-Pair Encoding vocabulary. It will automatically
+remove the BPE continuation markers and detokenize the output.
 
 .. code-block:: console
 
     > MODEL_DIR=wmt14.en-fr.fconv-py
     > fairseq-interactive \
         --path $MODEL_DIR/model.pt $MODEL_DIR \
-        --beam 5 --source-lang en --target-lang fr
+        --beam 5 --source-lang en --target-lang fr \
+        --tokenizer moses \
+        --bpe subword_nmt --bpe-codes $MODEL_DIR/bpecodes
     | loading model(s) from wmt14.en-fr.fconv-py/model.pt
     | [en] dictionary: 44206 types
     | [fr] dictionary: 44463 types
     | Type the input sentence and press return:
-    > Why is it rare to discover new marine mam@@ mal species ?
-    O       Why is it rare to discover new marine mam@@ mal species ?
-    H       -0.1525060087442398     Pourquoi est @-@ il rare de découvrir de nouvelles espèces de mammifères marins ?
-    P       -0.2221 -0.3122 -0.1289 -0.2673 -0.1711 -0.1930 -0.1101 -0.1660 -0.1003 -0.0740 -0.1101 -0.0814 -0.1238 -0.0985 -0.1288
+    Why is it rare to discover new marine mammal species?
+    S-0     Why is it rare to discover new marine mam@@ mal species ?
+    H-0     -0.0643349438905716     Pourquoi est-il rare de découvrir de nouvelles espèces de mammifères marins?
+    P-0     -0.0763 -0.1849 -0.0956 -0.0946 -0.0735 -0.1150 -0.1301 -0.0042 -0.0321 -0.0171 -0.0052 -0.0062 -0.0015
 
 This generation script produces three types of outputs: a line prefixed
 with *O* is a copy of the original source sentence; *H* is the

examples/translation/README.md

@@ -244,11 +244,12 @@ $ SRC=de
 $ sacrebleu --test-set iwslt17 --language-pair ${SRC}-en --echo src \
   | python scripts/spm_encode.py --model examples/translation/iwslt17.de_fr.en.bpe16k/sentencepiece.bpe.model \
   > iwslt17.test.${SRC}-en.${SRC}.bpe
-$ cat iwslt17.test.${SRC}-en.${SRC}.bpe | fairseq-interactive data-bin/iwslt17.de_fr.en.bpe16k/ \
-  --task multilingual_translation --source-lang ${SRC} --target-lang en \
-  --path checkpoints/multilingual_transformer/checkpoint_best.pt \
-  --buffer 2000 --batch-size 128 \
-  --beam 5 --remove-bpe=sentencepiece \
+$ cat iwslt17.test.${SRC}-en.${SRC}.bpe \
+  | fairseq-interactive data-bin/iwslt17.de_fr.en.bpe16k/ \
+      --task multilingual_translation --source-lang ${SRC} --target-lang en \
+      --path checkpoints/multilingual_transformer/checkpoint_best.pt \
+      --buffer 2000 --batch-size 128 \
+      --beam 5 --remove-bpe=sentencepiece \
   > iwslt17.test.${SRC}-en.en.sys
 $ grep ^H iwslt17.test.${SRC}-en.en.sys | cut -f3 \
   | sacrebleu --test-set iwslt17 --language-pair ${SRC}-en

examples/translation_moe/README.md

@@ -58,11 +58,12 @@ Next apply BPE on the fly and run generation for each expert:
 $ BPEROOT=examples/translation/subword-nmt/
 $ BPE_CODE=examples/translation/wmt17_en_de/code
 $ for EXPERT in $(seq 0 2); do \
-    cat wmt14-en-de.extra_refs.tok | grep ^S | cut -f 2 | \
-      python $BPEROOT/apply_bpe.py -c $BPE_CODE | \
-      fairseq-interactive data-bin/wmt17_en_de \
+    cat wmt14-en-de.extra_refs.tok \
+    | grep ^S | cut -f 2 \
+    | fairseq-interactive data-bin/wmt17_en_de \
         --path checkpoints/checkpoint_best.pt \
-        --beam 1 --remove-bpe \
+        --beam 1 \
+        --bpe subword_nmt --bpe-codes $BPE_CODE \
         --buffer-size 500 --max-tokens 6000 \
         --task translation_moe \
         --method hMoElp --mean-pool-gating-network \

fairseq/data/transforms/__init__.py

@@ -14,7 +14,7 @@ from fairseq import registry
 
 build_tokenizer, register_tokenizer, TOKENIZER_REGISTRY = registry.setup_registry(
     '--tokenizer',
-    default='space',
+    default=None,
 )
 
 

fairseq/data/transforms/moses_tokenizer.py

@@ -14,13 +14,13 @@ class MosesTokenizer(object):
     @staticmethod
     def add_args(parser):
         # fmt: off
-        parser.add_argument('-s', '--source-lang', default='en', metavar='SRC',
+        parser.add_argument('--moses-source-lang', default='en', metavar='SRC',
                             help='source language')
-        parser.add_argument('-t', '--target-lang', default='en', metavar='TARGET',
+        parser.add_argument('--moses-target-lang', default='en', metavar='TARGET',
                             help='target language')
-        parser.add_argument('--aggressive-dash-splits', action='store_true', default=False,
-                            help='triggers dash split rules')
-        parser.add_argument('--no-escape', action='store_true', default=False,
+        parser.add_argument('--moses-no-dash-splits', action='store_true', default=False,
+                            help='don\'t apply dash split rules')
+        parser.add_argument('--moses-no-escape', action='store_true', default=False,
                             help='don\'t perform HTML escaping on apostrophy, quotes, etc.')
         # fmt: on
 
@@ -28,17 +28,17 @@ class MosesTokenizer(object):
         self.args = args
         try:
             from sacremoses import MosesTokenizer, MosesDetokenizer
-            self.tok = MosesTokenizer(args.source_lang)
-            self.detok = MosesDetokenizer(args.target_lang)
+            self.tok = MosesTokenizer(args.moses_source_lang)
+            self.detok = MosesDetokenizer(args.moses_target_lang)
         except ImportError:
             raise ImportError('Please install Moses tokenizer with: pip install sacremoses')
 
     def encode(self, x: str) -> str:
         return self.tok.tokenize(
             x,
-            aggressive_dash_splits=self.args.aggressive_dash_splits,
+            aggressive_dash_splits=(not self.args.moses_no_dash_splits),
             return_str=True,
-            escape=(not self.args.no_escape),
+            escape=(not self.args.moses_no_escape),
         )
 
     def decode(self, x: str) -> str:

fairseq/data/transforms/space_tokenizer.py

@@ -17,7 +17,7 @@ class SpaceTokenizer(object):
         self.space_tok = re.compile(r"\s+")
 
     def encode(self, x: str) -> str:
-        return self.space_tok.sub(" ", x).strip().split()
+        return self.space_tok.sub(' ', x)
 
     def decode(self, x: str) -> str:
         return x

fairseq/data/transforms/subword_nmt_bpe.py

@@ -22,6 +22,8 @@ class SubwordNMTBPE(object):
         # fmt: on
 
     def __init__(self, args):
+        if args.bpe_codes is None:
+            raise ValueError('--bpe-codes is required for --bpe=subword_nmt')
         codes = file_utils.cached_path(args.bpe_codes)
         try:
             from subword_nmt import apply_bpe

fairseq/sequence_generator.py

@@ -160,7 +160,7 @@ class SequenceGenerator(object):
         scores_buf = scores.clone()
         tokens = src_tokens.data.new(bsz * beam_size, max_len + 2).long().fill_(self.pad)
         tokens_buf = tokens.clone()
-        tokens[:, 0] = bos_token or self.eos
+        tokens[:, 0] = self.eos if bos_token is None else bos_token
         attn, attn_buf = None, None
         nonpad_idxs = None
         if prefix_tokens is not None:
@@ -618,10 +618,8 @@ class EnsembleModel(torch.nn.Module):
             decoder_out[0].div_(temperature)
         attn = decoder_out[1]
         if type(attn) is dict:
-            attn = attn['attn']
+            attn = attn.get('attn', None)
         if attn is not None:
-            if type(attn) is dict:
-                attn = attn['attn']
             attn = attn[:, -1, :]
         probs = model.get_normalized_probs(decoder_out, log_probs=log_probs)
         probs = probs[:, -1, :]

interactive.py

@@ -15,6 +15,7 @@ import fileinput
 import torch
 
 from fairseq import checkpoint_utils, options, tasks, utils
+from fairseq.data import transforms
 
 
 Batch = namedtuple('Batch', 'ids src_tokens src_lengths')
@@ -101,17 +102,23 @@ def main(args):
     # Initialize generator
     generator = task.build_generator(args)
 
-    # Hack to support GPT-2 BPE
-    if args.remove_bpe == 'gpt2':
-        from fairseq.gpt2_bpe.gpt2_encoding import get_encoder
-        decoder = get_encoder(
-            'fairseq/gpt2_bpe/encoder.json',
-            'fairseq/gpt2_bpe/vocab.bpe',
-        )
-        encode_fn = lambda x: ' '.join(map(str, decoder.encode(x)))
-    else:
-        decoder = None
-        encode_fn = lambda x: x
+    # Handle tokenization and BPE
+    tokenizer = transforms.build_tokenizer(args)
+    bpe = transforms.build_bpe(args)
+
+    def encode_fn(x):
+        if tokenizer is not None:
+            x = tokenizer.encode(x)
+        if bpe is not None:
+            x = bpe.encode(x)
+        return x
+
+    def decode_fn(x):
+        if bpe is not None:
+            x = bpe.decode(x)
+        if tokenizer is not None:
+            x = tokenizer.decode(x)
+        return x
 
     # Load alignment dictionary for unknown word replacement
     # (None if no unknown word replacement, empty if no path to align dictionary)
@@ -162,8 +169,7 @@ def main(args):
                     tgt_dict=tgt_dict,
                     remove_bpe=args.remove_bpe,
                 )
-                if decoder is not None:
-                    hypo_str = decoder.decode(map(int, hypo_str.strip().split()))
+                hypo_str = decode_fn(hypo_str)
                 print('H-{}\t{}\t{}'.format(id, hypo['score'], hypo_str))
                 print('P-{}\t{}'.format(
                     id,