Source features support for V2.0

data/data_features/src-train.feat0

@@ -0,0 +1,3 @@
+A A A A B A A A C
+A B C D E
+C B A B

data/data_features/src-train.txt

@@ -0,0 +1,3 @@
+however, according to the logs, she is a hard-working.
+however, according to the logs,
+she is a hard-working.

data/data_features/src-val.feat0

@@ -0,0 +1 @@
+C B A B

data/data_features/src-val.txt

@@ -0,0 +1 @@
+she is a hard-working.

data/data_features/tgt-train.txt

@@ -0,0 +1,3 @@
+however, according to the logs, she is a hard-working.
+however, according to the logs,
+she is a hard-working.

data/data_features/tgt-val.txt

@@ -0,0 +1 @@
+she is a hard-working.

data/features_data.yaml

@@ -0,0 +1,11 @@
+# Corpus opts:
+data:
+    corpus_1:
+        path_src: data/data_features/src-train.txt
+        path_tgt: data/data_features/tgt-train.txt
+        src_feats:
+            feat0: data/data_features/src-train.feat0
+        transforms: [filterfeats, inferfeats]
+    valid:
+        path_src: data/data_features/src-val.txt
+        path_tgt: data/data_features/tgt-val.txt

docs/source/FAQ.md

@@ -477,3 +477,54 @@ Training options to perform vocabulary update are:
 * `-update_vocab`: set this option
 * `-reset_optim`: set the value to "states"
 * `-train_from`: checkpoint path
+
+
+## How can I use source word features?
+
+Extra information can be added to the words in the source sentences by defining word features. 
+
+Features should be defined in a separate file using blank spaces as a separator and with each row corresponding to a source sentence. An example of the input files:
+
+data.src
+```
+however, according to the logs, she is hard-working.
+```
+
+feat0.txt
+```
+A C C C C A A B
+```
+
+**Notes**
+- Prior tokenization is not necessary, features will be inferred by using the `FeatInferTransform` transform.
+- `FilterFeatsTransform` and `FeatInferTransform` are required in order to ensure the functionality.
+
+Sample config file:
+
+```
+data:
+    dummy:
+        path_src: data/train/data.src
+        path_tgt: data/train/data.tgt
+        src_feats:
+            feat_0: data/train/data.src.feat_0
+            feat_1: data/train/data.src.feat_1
+        transforms: [filterfeats, onmt_tokenize, inferfeats, filtertoolong]
+        weight: 1
+    valid:
+        path_src: data/valid/data.src
+        path_tgt: data/valid/data.tgt
+        src_feats:
+            feat_0: data/valid/data.src.feat_0
+            feat_1: data/valid/data.src.feat_1
+        transforms: [filterfeats, onmt_tokenize, inferfeats]
+
+# # Vocab opts
+src_vocab: exp/data.vocab.src
+tgt_vocab: exp/data.vocab.tgt
+src_feats_vocab: 
+    feat_0: exp/data.vocab.feat_0
+    feat_1: exp/data.vocab.feat_1
+feat_merge: "sum"
+
+```

onmt/bin/build_vocab.py

@@ -32,11 +32,13 @@ def build_vocab_main(opts):
     transforms = make_transforms(opts, transforms_cls, fields)
 
     logger.info(f"Counter vocab from {opts.n_sample} samples.")
-    src_counter, tgt_counter = build_vocab(
+    src_counter, tgt_counter, src_feats_counter = build_vocab(
         opts, transforms, n_sample=opts.n_sample)
 
     logger.info(f"Counters src:{len(src_counter)}")
     logger.info(f"Counters tgt:{len(tgt_counter)}")
+    for feat_name, feat_counter in src_feats_counter.items():
+        logger.info(f"Counters {feat_name}:{len(feat_counter)}")
 
     def save_counter(counter, save_path):
         check_path(save_path, exist_ok=opts.overwrite, log=logger.warning)
@@ -52,6 +54,9 @@ def save_counter(counter, save_path):
     else:
         save_counter(src_counter, opts.src_vocab)
         save_counter(tgt_counter, opts.tgt_vocab)
+
+    for k, v in src_feats_counter.items():
+        save_counter(v, opts.src_feats_vocab[k])
 
 
 def _get_parser():

onmt/constants.py

@@ -22,6 +22,7 @@ class CorpusName(object):
 class SubwordMarker(object):
     SPACER = '▁'
     JOINER = '￭'
+    CASE_MARKUP = ["｟mrk_case_modifier_C｠", "｟mrk_begin_case_region_U｠", "｟mrk_end_case_region_U｠"]
 
 
 class ModelTask(object):

onmt/inputters/corpus.py

@@ -7,7 +7,7 @@
 from torchtext.data import Dataset as TorchtextDataset, \
     Example as TorchtextExample
 
-from collections import Counter
+from collections import Counter, defaultdict
 from contextlib import contextmanager
 
 import multiprocessing as mp
@@ -74,6 +74,9 @@ def _process(item, is_train):
         maybe_example['tgt'] = ' '.join(maybe_example['tgt'])
         if 'align' in maybe_example:
             maybe_example['align'] = ' '.join(maybe_example['align'])
+        if 'src_feats' in maybe_example:
+            for k in maybe_example['src_feats'].keys():
+                maybe_example['src_feats'][k] = ' '.join(maybe_example['src_feats'][k])
         return maybe_example
 
     def _maybe_add_dynamic_dict(self, example, fields):
@@ -107,23 +110,32 @@ def __call__(self, bucket):
 class ParallelCorpus(object):
     """A parallel corpus file pair that can be loaded to iterate."""
 
-    def __init__(self, name, src, tgt, align=None):
+    def __init__(self, name, src, tgt, align=None, src_feats=None):
         """Initialize src & tgt side file path."""
         self.id = name
         self.src = src
         self.tgt = tgt
         self.align = align
+        self.src_feats = src_feats
 
     def load(self, offset=0, stride=1):
         """
         Load file and iterate by lines.
         `offset` and `stride` allow to iterate only on every
         `stride` example, starting from `offset`.
         """
+        if self.src_feats:
+            features_names = []
+            features_files = []
+            for feat_name, feat_path in self.src_feats.items():
+                features_names.append(feat_name)
+                features_files.append(open(feat_path, mode='rb'))
+        else:
+            features_files = []
         with exfile_open(self.src, mode='rb') as fs,\
                 exfile_open(self.tgt, mode='rb') as ft,\
                 exfile_open(self.align, mode='rb') as fa:
-            for i, (sline, tline, align) in enumerate(zip(fs, ft, fa)):
+            for i, (sline, tline, align, *features) in enumerate(zip(fs, ft, fa, *features_files)):
                 if (i % stride) == offset:
                     sline = sline.decode('utf-8')
                     tline = tline.decode('utf-8')
@@ -133,12 +145,18 @@ def load(self, offset=0, stride=1):
                     }
                     if align is not None:
                         example['align'] = align.decode('utf-8')
+                    if features:
+                        example["src_feats"] = dict()
+                        for j, feat in enumerate(features):
+                            example["src_feats"][features_names[j]] = feat.decode("utf-8")
                     yield example
+        for f in features_files:
+            f.close()
 
     def __str__(self):
         cls_name = type(self).__name__
-        return '{}({}, {}, align={})'.format(
-            cls_name, self.src, self.tgt, self.align)
+        return '{}({}, {}, align={}, src_feats={})'.format(
+            cls_name, self.src, self.tgt, self.align, self.src_feats)
 
 
 def get_corpora(opts, is_train=False):
@@ -150,7 +168,8 @@ def get_corpora(opts, is_train=False):
                     corpus_id,
                     corpus_dict["path_src"],
                     corpus_dict["path_tgt"],
-                    corpus_dict["path_align"])
+                    corpus_dict["path_align"],
+                    corpus_dict["src_feats"])
     else:
         if CorpusName.VALID in opts.data.keys():
             corpora_dict[CorpusName.VALID] = ParallelCorpus(
@@ -193,6 +212,9 @@ def _tokenize(self, stream):
             example['src'], example['tgt'] = src, tgt
             if 'align' in example:
                 example['align'] = example['align'].strip('\n').split()
+            if 'src_feats' in example:
+                for k in example['src_feats'].keys():
+                    example['src_feats'][k] = example['src_feats'][k].strip('\n').split()
             yield example
 
     def _transform(self, stream):
@@ -286,6 +308,7 @@ def build_sub_vocab(corpora, transforms, opts, n_sample, stride, offset):
     """Build vocab on (strided) subpart of the data."""
     sub_counter_src = Counter()
     sub_counter_tgt = Counter()
+    sub_counter_src_feats =  defaultdict(Counter)
     datasets_iterables = build_corpora_iters(
         corpora, transforms, opts.data,
         skip_empty_level=opts.skip_empty_level,
@@ -298,6 +321,9 @@ def build_sub_vocab(corpora, transforms, opts, n_sample, stride, offset):
                     build_sub_vocab.queues[c_name][offset].put("blank")
                 continue
             src_line, tgt_line = maybe_example['src'], maybe_example['tgt']
+            if 'src_feats' in maybe_example:
+                for feat_name, feat_line in maybe_example["src_feats"].items():
+                    sub_counter_src_feats[feat_name].update(feat_line.split(' '))
             sub_counter_src.update(src_line.split(' '))
             sub_counter_tgt.update(tgt_line.split(' '))
             if opts.dump_samples:
@@ -309,7 +335,7 @@ def build_sub_vocab(corpora, transforms, opts, n_sample, stride, offset):
                 break
         if opts.dump_samples:
             build_sub_vocab.queues[c_name][offset].put("break")
-    return sub_counter_src, sub_counter_tgt
+    return sub_counter_src, sub_counter_tgt, sub_counter_src_feats
 
 
 def init_pool(queues):
@@ -333,6 +359,7 @@ def build_vocab(opts, transforms, n_sample=3):
     corpora = get_corpora(opts, is_train=True)
     counter_src = Counter()
     counter_tgt = Counter()
+    counter_src_feats =  defaultdict(Counter)
     from functools import partial
     queues = {c_name: [mp.Queue(opts.vocab_sample_queue_size)
                        for i in range(opts.num_threads)]
@@ -349,13 +376,14 @@ def build_vocab(opts, transforms, n_sample=3):
         func = partial(
             build_sub_vocab, corpora, transforms,
             opts, n_sample, opts.num_threads)
-        for sub_counter_src, sub_counter_tgt in p.imap(
+        for sub_counter_src, sub_counter_tgt, sub_counter_src_feats in p.imap(
                 func, range(0, opts.num_threads)):
             counter_src.update(sub_counter_src)
             counter_tgt.update(sub_counter_tgt)
+            counter_src_feats.update(sub_counter_src_feats)
     if opts.dump_samples:
         write_process.join()
-    return counter_src, counter_tgt
+    return counter_src, counter_tgt, counter_src_feats
 
 
 def save_transformed_sample(opts, transforms, n_sample=3):

onmt/inputters/fields.py

@@ -8,11 +8,10 @@
 
 
 def _get_dynamic_fields(opts):
-    # NOTE: not support nfeats > 0 yet
-    src_nfeats = 0
-    tgt_nfeats = 0
+    # NOTE: not support tgt feats yet
+    tgt_feats = None
     with_align = hasattr(opts, 'lambda_align') and opts.lambda_align > 0.0
-    fields = get_fields('text', src_nfeats, tgt_nfeats,
+    fields = get_fields('text', opts.src_feats_vocab, tgt_feats,
                         dynamic_dict=opts.copy_attn,
                         src_truncate=opts.src_seq_length_trunc,
                         tgt_truncate=opts.tgt_seq_length_trunc,
@@ -33,6 +32,12 @@ def build_dynamic_fields(opts, src_specials=None, tgt_specials=None):
         opts.src_vocab, 'src', counters,
         min_freq=opts.src_words_min_frequency)
 
+    if opts.src_feats_vocab:
+        for feat_name, filepath in opts.src_feats_vocab.items():
+            _, _ = _load_vocab(
+                filepath, feat_name, counters,
+                min_freq=0)
+
     if opts.tgt_vocab:
         _tgt_vocab, _tgt_vocab_size = _load_vocab(
             opts.tgt_vocab, 'tgt', counters,

onmt/inputters/inputter.py

@@ -111,8 +111,8 @@ def get_task_spec_tokens(data_task, pad, bos, eos):
 
 def get_fields(
     src_data_type,
-    n_src_feats,
-    n_tgt_feats,
+    src_feats,
+    tgt_feats,
     pad=DefaultTokens.PAD,
     bos=DefaultTokens.BOS,
     eos=DefaultTokens.EOS,
@@ -125,11 +125,11 @@ def get_fields(
     """
     Args:
         src_data_type: type of the source input. Options are [text].
-        n_src_feats (int): the number of source features (not counting tokens)
+        src_feats (Optional[Dict]): source features dict containing their names
             to create a :class:`torchtext.data.Field` for. (If
             ``src_data_type=="text"``, these fields are stored together
             as a ``TextMultiField``).
-        n_tgt_feats (int): See above.
+        tgt_feats (Optional[Dict]): See above.
         pad (str): Special pad symbol. Used on src and tgt side.
         bos (str): Special beginning of sequence symbol. Only relevant
             for tgt.
@@ -158,7 +158,7 @@ def get_fields(
     task_spec_tokens = get_task_spec_tokens(data_task, pad, bos, eos)
 
     src_field_kwargs = {
-        "n_feats": n_src_feats,
+        "feats": src_feats,
         "include_lengths": True,
         "pad": task_spec_tokens["src"]["pad"],
         "bos": task_spec_tokens["src"]["bos"],
@@ -169,7 +169,7 @@ def get_fields(
     fields["src"] = fields_getters[src_data_type](**src_field_kwargs)
 
     tgt_field_kwargs = {
-        "n_feats": n_tgt_feats,
+        "feats": tgt_feats,
         "include_lengths": False,
         "pad": task_spec_tokens["tgt"]["pad"],
         "bos": task_spec_tokens["tgt"]["bos"],