docs: update annotation

Author: gongel

examples/machine_translation/transformer/predict_beamsearch_v2.py

@@ -59,8 +59,6 @@ def do_predict(args):
     test_loader, to_tokens = reader.create_infer_loader(args)
 
     # Define model
-    # `TransformerGenerator` automatically chioces using `FasterTransformer`
-    # (with jit building) or the slower verison `InferTransformerModel`.
     transformer = TransformerModel(
         src_vocab_size=args.src_vocab_size,
         trg_vocab_size=args.trg_vocab_size,
@@ -98,8 +96,6 @@ def do_predict(args):
 
     with paddle.no_grad():
         for (src_word, ) in test_loader:
-            # The shape of finished_seq is `[seq_len, batch_size, beam_size]`
-            # when `output_time_major` argument is `True` for TransformerGenerator.
             finished_seq, finished_scores = transformer.beam_search_v2(
                 src_word=src_word,
                 beam_size=args.beam_size,

paddlenlp/transformers/transformer/modeling.py

@@ -1028,9 +1028,8 @@ def grow_finished(finished_seq, finished_scores, finished_flags,
 
         # Accounting for corner case: It's possible that no sequence in alive for a
         # particular batch item ever reached EOS. In that case, we should just copy
-        # the contents of alive for that batch item. tf.reduce_any(finished_flags, 1)
-        # if 0, means that no sequence for that batch index had reached EOS. We need
-        # to do the same for the scores as well.
+        # the contents of alive for that batch item. if no sequence for that batch
+        # index had reached EOS. We need to do the same for the scores as well.
         finished_flags = paddle.any(paddle.cast(
             finished_flags, dtype='bool'),
                                     axis=1,