Make reader.py and infer.py support en-fr wordpiece data in Transformer

fluid/neural_machine_translation/transformer/infer.py

 import argparse
+import ast
 import numpy as np
 
 import paddle
@@ -11,6 +12,7 @@ from model import fast_decode as fast_decoder
 from config import *
 from train import pad_batch_data
 import reader
+import util
 
 
 def parse_args():
@@ -46,6 +48,14 @@ def parse_args():
         default=["<s>", "<e>", "<unk>"],
         nargs=3,
         help="The <bos>, <eos> and <unk> tokens in the dictionary.")
+    parser.add_argument(
+        "--use_wordpiece",
+        type=ast.literal_eval,
+        default=False,
+        help="The flag indicating if the data is wordpiece data. The EN-FR data we "
+        "provided is wordpiece data. For wordpiece data, converting ids to "
+        "original words is a little different and some special codes are "
+        "provided in util.py to do this.")
     parser.add_argument(
         'opts',
         help='See config.py for all options',
@@ -320,7 +330,7 @@ def post_process_seq(seq,
         seq)
 
 
-def py_infer(test_data, trg_idx2word):
+def py_infer(test_data, trg_idx2word, use_wordpiece):
     """
     Inference by beam search implented by python, while the calculations from
     symbols to probilities execute by Fluid operators.
@@ -399,7 +409,10 @@ def py_infer(test_data, trg_idx2word):
             seqs = map(post_process_seq, batch_seqs[i])
             scores = batch_scores[i]
             for seq in seqs:
-                print(" ".join([trg_idx2word[idx] for idx in seq]))
+                if use_wordpiece:
+                    print(util.subword_ids_to_str(seq, trg_idx2word))
+                else:
+                    print(" ".join([trg_idx2word[idx] for idx in seq]))
 
 
 def prepare_batch_input(insts, data_input_names, util_input_names, src_pad_idx,
@@ -465,7 +478,7 @@ def prepare_batch_input(insts, data_input_names, util_input_names, src_pad_idx,
     return input_dict
 
 
-def fast_infer(test_data, trg_idx2word):
+def fast_infer(test_data, trg_idx2word, use_wordpiece):
     """
     Inference by beam search decoder based solely on Fluid operators.
     """
@@ -520,7 +533,9 @@ def fast_infer(test_data, trg_idx2word):
                     trg_idx2word[idx]
                     for idx in post_process_seq(
                         np.array(seq_ids)[sub_start:sub_end])
-                ]))
+                ]) if not use_wordpiece else util.subword_ids_to_str(
+                    post_process_seq(np.array(seq_ids)[sub_start:sub_end]),
+                    trg_idx2word))
                 scores[i].append(np.array(seq_scores)[sub_end - 1])
                 print hyps[i][-1]
                 if len(hyps[i]) >= InferTaskConfig.n_best:
@@ -548,7 +563,7 @@ def infer(args, inferencer=fast_infer):
         clip_last_batch=False)
     trg_idx2word = test_data.load_dict(
         dict_path=args.trg_vocab_fpath, reverse=True)
-    inferencer(test_data, trg_idx2word)
+    inferencer(test_data, trg_idx2word, args.use_wordpiece)
 
 
 if __name__ == "__main__":

fluid/neural_machine_translation/transformer/reader.py

@@ -116,9 +116,12 @@ class DataReader(object):
     :param use_token_batch: Whether to produce batch data according to
         token number.
     :type use_token_batch: bool
-    :param delimiter: The delimiter used to split source and target in each
-        line of data file.
-    :type delimiter: basestring
+    :param field_delimiter: The delimiter used to split source and target in
+        each line of data file.
+    :type field_delimiter: basestring
+    :param token_delimiter: The delimiter used to split tokens in source or
+        target sentences.
+    :type token_delimiter: basestring
     :param start_mark: The token representing for the beginning of
         sentences in dictionary.
     :type start_mark: basestring
@@ -145,7 +148,8 @@ class DataReader(object):
                  shuffle=True,
                  shuffle_batch=False,
                  use_token_batch=False,
-                 delimiter="\t",
+                 field_delimiter="\t",
+                 token_delimiter=" ",
                  start_mark="<s>",
                  end_mark="<e>",
                  unk_mark="<unk>",
@@ -164,7 +168,8 @@ class DataReader(object):
         self._shuffle_batch = shuffle_batch
         self._min_length = min_length
         self._max_length = max_length
-        self._delimiter = delimiter
+        self._field_delimiter = field_delimiter
+        self._token_delimiter = token_delimiter
         self._epoch_batches = []
 
         src_seq_words, trg_seq_words = self._load_data(fpattern, tar_fname)
@@ -196,7 +201,7 @@ class DataReader(object):
         trg_seq_words = []
 
         for line in f_obj:
-            fields = line.strip().split(self._delimiter)
+            fields = line.strip().split(self._field_delimiter)
 
             if (not self._only_src and len(fields) != 2) or (self._only_src and
                                                              len(fields) != 1):
@@ -207,7 +212,7 @@ class DataReader(object):
             max_len = -1
 
             for i, seq in enumerate(fields):
-                seq_words = seq.split()
+                seq_words = seq.split(self._token_delimiter)
                 max_len = max(max_len, len(seq_words))
                 if len(seq_words) == 0 or \
                         len(seq_words) < self._min_length or \