Merge pull request #809 from guoshengCS/refine-transformer-token

Refine the inference to output special tokens optionally in Transformer

fluid/neural_machine_translation/transformer/config.py

@@ -31,6 +31,11 @@ class InferTaskConfig(object):
     # the number of decoded sentences to output.
     n_best = 1
 
+    # the flags indicating whether to output the special tokens.
+    output_bos = False
+    output_eos = False
+    output_unk = False
+
     # the directory for loading the trained model.
     model_path = "trained_models/pass_1.infer.model"
 
@@ -56,6 +61,8 @@ class ModelHyperParams(object):
     bos_idx = 0
     # index for <eos> token
     eos_idx = 1
+    # index for <unk> token
+    unk_idx = 2
 
     # position value corresponding to the <pad> token.
     pos_pad_idx = 0

fluid/neural_machine_translation/transformer/infer.py

@@ -11,10 +11,25 @@ from config import InferTaskConfig, ModelHyperParams, \
 from train import pad_batch_data
 
 
-def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
-                    decoder, dec_in_names, dec_out_names, beam_size, max_length,
-                    n_best, batch_size, n_head, src_pad_idx, trg_pad_idx,
-                    bos_idx, eos_idx):
+def translate_batch(exe,
+                    src_words,
+                    encoder,
+                    enc_in_names,
+                    enc_out_names,
+                    decoder,
+                    dec_in_names,
+                    dec_out_names,
+                    beam_size,
+                    max_length,
+                    n_best,
+                    batch_size,
+                    n_head,
+                    src_pad_idx,
+                    trg_pad_idx,
+                    bos_idx,
+                    eos_idx,
+                    unk_idx,
+                    output_unk=True):
     """
     Run the encoder program once and run the decoder program multiple times to
     implement beam search externally.
@@ -48,7 +63,7 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
     # size of feeded batch is changing.
     beam_map = range(batch_size)
 
-    def beam_backtrace(prev_branchs, next_ids, n_best=beam_size, add_bos=True):
+    def beam_backtrace(prev_branchs, next_ids, n_best=beam_size):
         """
         Decode and select n_best sequences for one instance by backtrace.
         """
@@ -60,7 +75,8 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
                 seq.append(next_ids[j][k])
                 k = prev_branchs[j][k]
             seq = seq[::-1]
-            seq = [bos_idx] + seq if add_bos else seq
+            # Add the <bos>, since next_ids don't include the <bos>.
+            seq = [bos_idx] + seq
             seqs.append(seq)
         return seqs
 
@@ -114,8 +130,7 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
         trg_cur_len = len(next_ids[0]) + 1  # include the <bos>
         trg_words = np.array(
             [
-                beam_backtrace(
-                    prev_branchs[beam_idx], next_ids[beam_idx], add_bos=True)
+                beam_backtrace(prev_branchs[beam_idx], next_ids[beam_idx])
                 for beam_idx in active_beams
             ],
             dtype="int64")
@@ -167,6 +182,8 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
         predict_all = (predict_all + scores[beam_map].reshape(
             [len(beam_map) * beam_size, -1])).reshape(
                 [len(beam_map), beam_size, -1])
+        if not output_unk:  # To exclude the <unk> token.
+            predict_all[:, :, unk_idx] = -1e9
         active_beams = []
         for inst_idx, beam_idx in enumerate(beam_map):
             predict = (predict_all[inst_idx, :, :]
@@ -187,7 +204,10 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
         dec_in_data = update_dec_in_data(dec_in_data, next_ids, active_beams)
 
     # Decode beams and select n_best sequences for each instance by backtrace.
-    seqs = [beam_backtrace(prev_branchs[beam_idx], next_ids[beam_idx], n_best)]
+    seqs = [
+        beam_backtrace(prev_branchs[beam_idx], next_ids[beam_idx], n_best)
+        for beam_idx in range(batch_size)
+    ]
 
     return seqs, scores[:, :n_best].tolist()
 
@@ -254,17 +274,47 @@ def main():
     trg_idx2word = paddle.dataset.wmt16.get_dict(
         "de", dict_size=ModelHyperParams.trg_vocab_size, reverse=True)
 
+    def post_process_seq(seq,
+                         bos_idx=ModelHyperParams.bos_idx,
+                         eos_idx=ModelHyperParams.eos_idx,
+                         output_bos=InferTaskConfig.output_bos,
+                         output_eos=InferTaskConfig.output_eos):
+        """
+        Post-process the beam-search decoded sequence. Truncate from the first
+        <eos> and remove the <bos> and <eos> tokens currently.
+        """
+        eos_pos = len(seq) - 1
+        for i, idx in enumerate(seq):
+            if idx == eos_idx:
+                eos_pos = i
+                break
+        seq = seq[:eos_pos + 1]
+        return filter(
+            lambda idx: (output_bos or idx != bos_idx) and \
+                (output_eos or idx != eos_idx),
+            seq)
+
     for batch_id, data in enumerate(test_data()):
         batch_seqs, batch_scores = translate_batch(
-            exe, [item[0] for item in data], encoder_program,
-            encoder_input_data_names, [enc_output.name], decoder_program,
-            decoder_input_data_names, [predict.name], InferTaskConfig.beam_size,
-            InferTaskConfig.max_length, InferTaskConfig.n_best,
-            len(data), ModelHyperParams.n_head, ModelHyperParams.src_pad_idx,
-            ModelHyperParams.trg_pad_idx, ModelHyperParams.bos_idx,
-            ModelHyperParams.eos_idx)
+            exe, [item[0] for item in data],
+            encoder_program,
+            encoder_input_data_names, [enc_output.name],
+            decoder_program,
+            decoder_input_data_names, [predict.name],
+            InferTaskConfig.beam_size,
+            InferTaskConfig.max_length,
+            InferTaskConfig.n_best,
+            len(data),
+            ModelHyperParams.n_head,
+            ModelHyperParams.src_pad_idx,
+            ModelHyperParams.trg_pad_idx,
+            ModelHyperParams.bos_idx,
+            ModelHyperParams.eos_idx,
+            ModelHyperParams.unk_idx,
+            output_unk=InferTaskConfig.output_unk)
         for i in range(len(batch_seqs)):
-            seqs = batch_seqs[i]
+            # Post-process the beam-search decoded sequences.
+            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]))