Support batched input for decoder start ids (huggingface#28887)

* support batched input for decoder start ids

* Fix typos

Co-authored-by: Joao Gante <[email protected]>

* minor changes

* fix: decoder_start_id as list

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Co-authored-by: Joao Gante <[email protected]>

src/transformers/generation/configuration_utils.py

@@ -233,8 +233,11 @@ class GenerationConfig(PushToHubMixin):
         encoder_no_repeat_ngram_size (`int`, *optional*, defaults to 0):
             If set to int > 0, all ngrams of that size that occur in the `encoder_input_ids` cannot occur in the
             `decoder_input_ids`.
-        decoder_start_token_id (`int`, *optional*):
-            If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token.
+        decoder_start_token_id (`Union[int, List[int]]`, *optional*):
+            If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token or a list of length
+            `batch_size`. Indicating a list enables different start ids for each element in the batch
+            (e.g. multilingual models with different target languages in one batch)
+
 
         > Generation parameters exclusive to [assistant generation](https://arxiv.org/abs/2211.17192)
 

src/transformers/generation/utils.py

@@ -501,7 +501,7 @@ def _prepare_decoder_input_ids_for_generation(
         batch_size: int,
         model_input_name: str,
         model_kwargs: Dict[str, torch.Tensor],
-        decoder_start_token_id: int = None,
+        decoder_start_token_id: Union[int, List[int]] = None,
         bos_token_id: int = None,
         device: torch.device = None,
     ) -> Tuple[torch.LongTensor, Dict[str, torch.Tensor]]:
@@ -519,7 +519,17 @@ def _prepare_decoder_input_ids_for_generation(
         decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
         if device is None:
             device = self.device
-        decoder_input_ids_start = torch.ones((batch_size, 1), dtype=torch.long, device=device) * decoder_start_token_id
+        if isinstance(decoder_start_token_id, list):
+            if len(decoder_start_token_id) != batch_size:
+                raise ValueError(
+                    f"`decoder_start_token_id` expcted to have length {batch_size} but got {len(decoder_start_token_id)}"
+                )
+            decoder_input_ids_start = torch.tensor(decoder_start_token_id, dtype=torch.long, device=device)
+            decoder_input_ids_start = decoder_input_ids_start.view(-1, 1)
+        else:
+            decoder_input_ids_start = (
+                torch.ones((batch_size, 1), dtype=torch.long, device=device) * decoder_start_token_id
+            )
 
         # no user input -> use decoder_start_token_id as decoder_input_ids
         if decoder_input_ids is None:
@@ -531,7 +541,13 @@ def _prepare_decoder_input_ids_for_generation(
             pass
         # user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
         # decoder_attention_mask if provided)
-        elif (decoder_input_ids[:, 0] != decoder_start_token_id).all().item():
+        elif (
+            isinstance(decoder_start_token_id, int)
+            and (decoder_input_ids[:, 0] != decoder_start_token_id).all().item()
+        ) or (
+            isinstance(decoder_start_token_id, torch.Tensor)
+            and (decoder_input_ids[:, 0] != decoder_start_token_id[:, 0]).all().item()
+        ):
             decoder_input_ids = torch.cat([decoder_input_ids_start, decoder_input_ids], dim=-1)
             if "decoder_attention_mask" in model_kwargs:
                 decoder_attention_mask = model_kwargs["decoder_attention_mask"]
@@ -543,7 +559,9 @@ def _prepare_decoder_input_ids_for_generation(
 
         return decoder_input_ids, model_kwargs
 
-    def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
+    def _get_decoder_start_token_id(
+        self, decoder_start_token_id: Union[int, List[int]] = None, bos_token_id: int = None
+    ) -> int:
         decoder_start_token_id = (
             decoder_start_token_id
             if decoder_start_token_id is not None

tests/generation/test_utils.py

@@ -3163,6 +3163,26 @@ def test_constrained_beam_search_mixin_type_checks(self):
         with self.assertRaises(ValueError):
             model.generate(input_ids, force_words_ids=[[[-1]]])
 
+    def test_batched_decoder_start_id(self):
+        # PT-only test: TF doesn't support batched_decoder_start_id
+        articles = [
+            "Justin Timberlake and Jessica Biel, welcome to parenthood.",
+            "Michael Phelps is arguably the most decorated Olympian of all time.",
+        ]
+        bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
+        bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
+            torch_device
+        )
+        input_ids = bart_tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
+        decoder_start_token_id = bart_model.generation_config.decoder_start_token_id
+        decoder_start_token_id_batch = [decoder_start_token_id] * input_ids.shape[0]
+
+        outputs = bart_model.generate(input_ids, decoder_start_token_id=decoder_start_token_id)
+
+        outputs_batched_ids = bart_model.generate(input_ids, decoder_start_token_id=decoder_start_token_id_batch)
+
+        self.assertListEqual(outputs.tolist(), outputs_batched_ids.tolist())
+
     def test_contrastive_search_batched(self):
         # PT-only test: TF doesn't have constrained beam search
         # Tests that contrastive search works with batched inputs (i.e. has the same output as for non-batched inputs)