seq2seq/run_eval.py can take decoder_start_token_id (#5949)

examples/seq2seq/finetune.py

@@ -327,6 +327,7 @@ class TranslationModule(SummarizationModule):
         self.dataset_kwargs["tgt_lang"] = hparams.tgt_lang
         if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, MBartTokenizer):
             self.decoder_start_token_id = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
+            self.model.config.decoder_start_token_id = self.decoder_start_token_id
         if isinstance(self.tokenizer, MBartTokenizer):
             self.dataset_class = MBartDataset
 

examples/seq2seq/run_eval.py

@@ -30,6 +30,7 @@ def generate_summaries_or_translations(
     device: str = DEFAULT_DEVICE,
     fp16=False,
     task="summarization",
+    decoder_start_token_id=None,
     **gen_kwargs,
 ) -> None:
     fout = Path(out_file).open("w", encoding="utf-8")
@@ -37,6 +38,8 @@ def generate_summaries_or_translations(
     model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
     if fp16:
         model = model.half()
+    if decoder_start_token_id is None:
+        decoder_start_token_id = gen_kwargs.pop("decoder_start_token_id", None)
 
     tokenizer = AutoTokenizer.from_pretrained(model_name)
 
@@ -48,7 +51,12 @@ def generate_summaries_or_translations(
             batch = [model.config.prefix + text for text in batch]
         batch = tokenizer(batch, return_tensors="pt", truncation=True, padding="max_length").to(device)
         input_ids, attention_mask = trim_batch(**batch, pad_token_id=tokenizer.pad_token_id)
-        summaries = model.generate(input_ids=input_ids, attention_mask=attention_mask, **gen_kwargs)
+        summaries = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_start_token_id=decoder_start_token_id,
+            **gen_kwargs,
+        )
         dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
         for hypothesis in dec:
             fout.write(hypothesis + "\n")
@@ -66,6 +74,13 @@ def run_generate():
     parser.add_argument("--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.")
     parser.add_argument("--task", type=str, default="summarization", help="typically translation or summarization")
     parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
+    parser.add_argument(
+        "--decoder_start_token_id",
+        type=int,
+        default=None,
+        required=False,
+        help="decoder_start_token_id (otherwise will look at config)",
+    )
     parser.add_argument(
         "--n_obs", type=int, default=-1, required=False, help="How many observations. Defaults to all."
     )
@@ -83,6 +98,7 @@ def run_generate():
         device=args.device,
         fp16=args.fp16,
         task=args.task,
+        decoder_start_token_id=args.decoder_start_token_id,
     )
     if args.reference_path is None:
         return

src/transformers/tokenization_utils_base.py

@@ -2255,8 +2255,23 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
 
         return encoded_inputs
 
-    def batch_decode(self, sequences: List[List[int]], **kwargs) -> List[str]:
-        return [self.decode(seq, **kwargs) for seq in sequences]
+    def batch_decode(
+        self, sequences: List[List[int]], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True
+    ) -> List[str]:
+        """
+        Convert a list of lists of token ids into a list of strings by calling decode.
+
+        Args:
+            token_ids: list of tokenized input ids. Can be obtained using the `encode` or `encode_plus` methods.
+            skip_special_tokens: if set to True, will replace special tokens.
+            clean_up_tokenization_spaces: if set to True, will clean up the tokenization spaces.
+        """
+        return [
+            self.decode(
+                seq, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces
+            )
+            for seq in sequences
+        ]
 
     def decode(
         self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True