fix OpenNMT#2329

onmt/decoders/transformer.py

@@ -737,6 +737,8 @@ def _init_cache(self, enc_out):
                 )
                 if hasattr(layer.self_attn, "rope"):
                     layer.self_attn.rope = layer.self_attn.rope.to(enc_out.device)
+                    layer.self_attn.cos = layer.self_attn.cos.to(enc_out.device)
+                    layer.self_attn.sin = layer.self_attn.sin.to(enc_out.device)
 
 
 class TransformerLMDecoderLayer(TransformerDecoderLayerBase):

onmt/inputters/text_corpus.py

@@ -130,7 +130,7 @@ def get_corpora(opts, task=CorpusTask.TRAIN, src=None, tgt=None, align=None):
             corpora_dict[CorpusName.VALID] = ParallelCorpus(
                 CorpusName.VALID,
                 opts.data[CorpusName.VALID]["path_src"],
-                opts.data[CorpusName.VALID]["path_tgt"],
+                opts.data[CorpusName.VALID]["path_tgt"] if tgt is None else None,
                 opts.data[CorpusName.VALID]["path_align"],
                 n_src_feats=opts.n_src_feats,
                 src_feats_defaults=opts.src_feats_defaults,

onmt/train_single.py

@@ -34,6 +34,7 @@ def prepare_transforms_vocabs(opt, transforms_cls):
         opt.transforms = validset_transforms
         if opt.data.get("valid", {}).get("tgt_prefix", None):
             opt.tgt_prefix = opt.data.get("valid", {}).get("tgt_prefix", None)
+            opt.tgt_file_prefix = True
         if opt.data.get("valid", {}).get("src_prefix", None):
             opt.src_prefix = opt.data.get("valid", {}).get("src_prefix", None)
         if opt.data.get("valid", {}).get("tgt_suffix", None):

onmt/utils/scoring_utils.py

@@ -2,7 +2,7 @@
 import os
 from onmt.utils.parse import ArgumentParser
 from onmt.translate import GNMTGlobalScorer, Translator
-from onmt.opts import translate_opts
+from onmt.opts import config_opts, translate_opts
 from onmt.constants import CorpusTask
 from onmt.inputters.dynamic_iterator import build_dynamic_dataset_iter
 from onmt.transforms import get_transforms_cls, make_transforms, TransformPipe
@@ -49,12 +49,16 @@ def translate(self, model, gpu_rank, step):
         # Translator #
         # ########## #
 
-        # Set translation options
+        # Set "default" translation options on empty cfgfile
         parser = ArgumentParser()
+        config_opts(parser)
         translate_opts(parser)
         base_args = ["-model", "dummy"] + ["-src", "dummy"]
         opt = parser.parse_args(base_args)
         opt.gpu = gpu_rank
+        if hasattr(self.opt, "tgt_file_prefix"):
+            opt.tgt_file_prefix = self.opt.tgt_file_prefix
+        opt.beam_size = 1  # prevent OOM when GPU is almost full at training
         ArgumentParser.validate_translate_opts(opt)
 
         # Build translator from options
@@ -85,25 +89,26 @@ def translate(self, model, gpu_rank, step):
         model_opt.num_workers = 0
         model_opt.tgt = None
 
+        # Retrieve raw references and sources
+        with codecs.open(
+            model_opt.data["valid"]["path_tgt"], "r", encoding="utf-8"
+        ) as f:
+            raw_refs = [line.strip("\n") for line in f if line.strip("\n")]
+        with codecs.open(
+            model_opt.data["valid"]["path_src"], "r", encoding="utf-8"
+        ) as f:
+            raw_srcs = [line.strip("\n") for line in f if line.strip("\n")]
+
         valid_iter = build_dynamic_dataset_iter(
             model_opt,
             transforms_cls,
             translator.vocabs,
             task=CorpusTask.VALID,
+            tgt="",  # This force to clear the target side (needed when using tgt_file_prefix)
             copy=model_opt.copy_attn,
             device_id=opt.gpu,
         )
 
-        # Retrieve raw references and sources
-        with codecs.open(
-            valid_iter.corpora_info["valid"]["path_tgt"], "r", encoding="utf-8"
-        ) as f:
-            raw_refs = [line.strip("\n") for line in f if line.strip("\n")]
-        with codecs.open(
-            valid_iter.corpora_info["valid"]["path_src"], "r", encoding="utf-8"
-        ) as f:
-            raw_srcs = [line.strip("\n") for line in f if line.strip("\n")]
-
         # ########### #
         # Predictions #
         # ########### #